From 7054ce589a6feaa108f4f4122f9544cbafa31a49 Mon Sep 17 00:00:00 2001 From: Ajit Johnson Nirmal Date: Sat, 27 Apr 2024 21:24:04 -0400 Subject: [PATCH] minor review fixes --- README.md | 2 +- docs/Getting Started.md | 2 +- paper/paper.bib | 1515 ++++++++++++++++++++++++++++++++------- paper/paper.md | 2 +- 4 files changed, 1278 insertions(+), 243 deletions(-) diff --git a/README.md b/README.md index d2de055f..a67ea612 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ Install `scimap` directly into an activated virtual environment: Here's how you can install both using pip: ```python -pip install scimap[napari] +pip install "scimap[napari]" ``` **If you encounter a problem with PyQt6 during the installation, you can install `scimap` alone first. Later on, if you find you need `napari`, you can go ahead and install it by itself.** diff --git a/docs/Getting Started.md b/docs/Getting Started.md index 5e241970..634a0267 100644 --- a/docs/Getting Started.md +++ b/docs/Getting Started.md @@ -28,7 +28,7 @@ Install `scimap` directly into an activated virtual environment: Here's how you can install both using pip: ```python -pip install scimap[napari] +pip install "scimap[napari]" ``` **If you encounter a problem with PyQt6 during the installation, you can install `scimap` alone first. Later on, if you find you need `napari`, you can go ahead and install it by itself.** diff --git a/paper/paper.bib b/paper/paper.bib index 0f7625eb..b4265add 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,147 +1,151 @@ -@article{noauthor_catching_2022, - title = {Catching up with multiplexed tissue imaging}, - volume = {19}, - rights = {2022 Springer Nature America, Inc.}, - issn = {1548-7105}, - url = {https://www.nature.com/articles/s41592-022-01428-z}, - doi = {10.1038/s41592-022-01428-z}, - abstract = {Highly multiplexed tissue imaging continues to show its power for biomedical discovery. In this issue, we publish tools and guidance for implementing this class of methods and reporting subsequent results.}, - pages = {259--259}, - number = {3}, - journaltitle = {Nature Methods}, - shortjournal = {Nat Methods}, - urldate = {2024-04-01}, - date = {2022-03}, - langid = {english}, - note = {Publisher: Nature Publishing Group}, - keywords = {Fluorescence imaging, Histology, Immunocytochemistry, Scientific community}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\X8ANJRMA\\2022 - Catching up with multiplexed tissue imaging.pdf:application/pdf}, +@article{maliga_immune_2024, + title = {Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade Using Tissue Cyclic Immunofluorescence: A Pilot Study}, + issn = {1523-1747}, + doi = {10.1016/j.jid.2024.01.024}, + shorttitle = {Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade Using Tissue Cyclic Immunofluorescence}, + pages = {S0022--202X(24)00107--6}, + journaltitle = {The Journal of Investigative Dermatology}, + shortjournal = {J Invest Dermatol}, + author = {Maliga, Zoltan and Kim, Daniel Y. and Bui, Ai-Tram N. and Lin, Jia-Ren and Dewan, Anna K. and Jadeja, Saagar and Murphy, George F. and Nirmal, Ajit J. and Lian, Christine G. and Sorger, Peter K. and {LeBoeuf}, Nicole R.}, + date = {2024-02-14}, + pmid = {38360200}, + keywords = {Immunotherapy, Cutaneous adverse events, Immune checkpoint blockade, Immune-related adverse events, Tissue cyclic immunofluorescence}, } -@article{liu_analysis_2022, - title = {Analysis and Visualization of Spatial Transcriptomic Data}, +@article{gaglia_lymphocyte_2023, + title = {Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma}, + volume = {41}, + issn = {1878-3686}, + doi = {10.1016/j.ccell.2023.03.015}, + abstract = {Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. {CXCR}3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored {TCF}1+ {PD}-1+ progenitor {CD}8+ T cells involved in responses to immune checkpoint blockade ({ICB}) therapy. Upon treatment of mice with {ICB} or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic {CD}8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of {CD}8+ T cell anti-tumor responses.}, + pages = {871--886.e10}, + number = {5}, + journaltitle = {Cancer Cell}, + shortjournal = {Cancer Cell}, + author = {Gaglia, Giorgio and Burger, Megan L. and Ritch, Cecily C. and Rammos, Danae and Dai, Yang and Crossland, Grace E. and Tavana, Sara Z. and Warchol, Simon and Jaeger, Alex M. and Naranjo, Santiago and Coy, Shannon and Nirmal, Ajit J. and Krueger, Robert and Lin, Jia-Ren and Pfister, Hanspeter and Sorger, Peter K. and Jacks, Tyler and Santagata, Sandro}, + date = {2023-05-08}, + pmid = {37059105}, + pmcid = {PMC10193529}, + keywords = {cancer vaccines, computational biology, {CyCIF}, immunotherapy, lung adenocarcinoma, multimodal data integration, multiplexed imaging, spatial biology, spatial profiling, systems biology, Humans, Animals, Mice, Immunotherapy, Lung Neoplasms, {CD}8-Positive T-Lymphocytes, Adenocarcinoma of Lung, Immunity}, + file = {Submitted Version:/Users/aj/Zotero/storage/A4Y42LBZ/Gaglia et al. - 2023 - Lymphocyte networks are dynamic cellular communiti.pdf:application/pdf}, +} + +@article{nirmal_spatial_2022, + title = {The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution}, volume = {12}, - issn = {1664-8021}, - url = {https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.785290/full}, - doi = {10.3389/fgene.2021.785290}, - abstract = {{\textless}p{\textgreater}Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. {RNA} molecules are measured by {\textless}italic{\textgreater}in situ{\textless}/italic{\textgreater} sequencing, {\textless}italic{\textgreater}in situ{\textless}/italic{\textgreater} hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.{\textless}/p{\textgreater}}, - journaltitle = {Frontiers in Genetics}, - shortjournal = {Front. Genet.}, - author = {Liu, Boxiang and Li, Yanjun and Zhang, Liang}, + issn = {2159-8274}, + url = {https://doi.org/10.1158/2159-8290.CD-21-1357}, + doi = {10.1158/2159-8290.CD-21-1357}, + abstract = {Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor–stromal boundary. This environment is established by cytokine gradients that promote expression of {MHC}-{II} and {IDO}1, and by {PD}1–{PDL}1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen.The reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classic histopathology, spatial profiling of proteins and {mRNA} reveals recurrent morphologic and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell–cell contact.This article is highlighted in the In This Issue feature, p. 1397}, + pages = {1518--1541}, + number = {6}, + journaltitle = {Cancer Discovery}, + shortjournal = {Cancer Discovery}, + author = {Nirmal, Ajit J. and Maliga, Zoltan and Vallius, Tuulia and Quattrochi, Brian and Chen, Alyce A. and Jacobson, Connor A. and Pelletier, Roxanne J. and Yapp, Clarence and Arias-Camison, Raquel and Chen, Yu-An and Lian, Christine G. and Murphy, George F. and Santagata, Sandro and Sorger, Peter K.}, urldate = {2024-04-01}, - date = {2022-01-27}, - note = {Publisher: Frontiers}, - keywords = {Cell-to-cell interaction, clustering, Single-cell {RNA}-seq ({scRNA}-seq), Spatial expression pattern, Spatial transcriptomics, visualization}, - file = {Full Text:C\:\\Users\\aj\\Zotero\\storage\\KFMS2MHW\\Liu et al. - 2022 - Analysis and Visualization of Spatial Transcriptom.pdf:application/pdf}, + date = {2022-06-02}, + file = {Full Text PDF:/Users/aj/Zotero/storage/NPFVJ8J8/Nirmal et al. - 2022 - The Spatial Landscape of Progression and Immunoedi.pdf:application/pdf;Snapshot:/Users/aj/Zotero/storage/9VX8ZHYQ/The-Spatial-Landscape-of-Progression-and.html:text/html}, } -@article{angelo_multiplexed_2014, - title = {Multiplexed ion beam imaging of human breast tumors}, - volume = {20}, - rights = {2014 Springer Nature America, Inc.}, - issn = {1546-170X}, - url = {https://www.nature.com/articles/nm.3488}, - doi = {10.1038/nm.3488}, - abstract = {The work of Michael Angelo and colleagues uses multiplexed ion beam imaging ({MIBI}) to localize and visualize protein expression in a manner analogous to immunohistochemistry ({IHC}) while circumventing some of the limitations of conventional {IHC} with clinical samples. {MIBI} uses secondary ion mass spectrometry to image antibodies tagged with isotopically pure elemental metal reporters, expanding the number of targets that can be analyzed simultaneously to about 100. The approach, used here to image breast tumor tissue sections, offers over a five-log dynamic range and compatibility with standard formalin-fixed, paraffin-embedded tissue sections.}, - pages = {436--442}, - number = {4}, - journaltitle = {Nature Medicine}, - shortjournal = {Nat Med}, - author = {Angelo, Michael and Bendall, Sean C. and Finck, Rachel and Hale, Matthew B. and Hitzman, Chuck and Borowsky, Alexander D. and Levenson, Richard M. and Lowe, John B. and Liu, Scot D. and Zhao, Shuchun and Natkunam, Yasodha and Nolan, Garry P.}, +@misc{yapp_multiplexed_2024, + title = {Multiplexed 3D Analysis of Immune States and Niches in Human Tissue}, + rights = {© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, + url = {https://www.biorxiv.org/content/10.1101/2023.11.10.566670v3}, + doi = {10.1101/2023.11.10.566670}, + abstract = {Tissue homeostasis and the emergence of disease are controlled by changes in the proportions of resident and recruited cells, their organization into cellular neighbourhoods, and their interactions with acellular tissue components. Highly multiplexed tissue profiling (spatial omics) makes it possible to study this microenvironment in situ, usually in 4-5 micron thick sections (the standard histopathology format). Microscopy-based tissue profiling is commonly performed at a resolution sufficient to determine cell types but not to detect subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking. Here we describe a high-resolution 3D imaging approach able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. This approach combines cyclic immunofluorescence ({CyCIF}) imaging of over 50 markers with confocal microscopy of archival human tissue thick enough (30-40 microns) to fully encompass two or more layers of intact cells. 3D imaging of entire cell volumes substantially improves the accuracy of cell phenotyping and allows cell proximity to be scored using plasma membrane apposition, not just nuclear position. In pre-invasive melanoma in situ, precise phenotyping shows that adjacent melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. In this and metastatic melanoma, mature and precursor T cells engage in an unexpectedly diverse array of juxtracrine and membrane-membrane interactions as well as looser neighbourhood associations whose morphologies reveal functional states. These data provide new insight into the transitions occurring during early tumour formation and immunoediting and demonstrate the potential for phenotyping of tissues at a level of detail previously restricted to cultured cells and organoids.}, + publisher = {{bioRxiv}}, + author = {Yapp, Clarence and Nirmal, Ajit J. and Zhou, Felix Yuran and Maliga, Zoltan and Tefft, Juliann and Llopis, Paula Montero and Murphy, George F. and Lian, Christine and Danuser, Gaudenz and Santagata, Sandro and Sorger, Peter Karl}, urldate = {2024-04-01}, - date = {2014-04}, + date = {2024-03-28}, langid = {english}, - note = {Publisher: Nature Publishing Group}, - keywords = {Computational biology and bioinformatics, Imaging, Immunohistochemistry}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\3BG8JWWH\\Angelo et al. - 2014 - Multiplexed ion beam imaging of human breast tumor.pdf:application/pdf}, + note = {Pages: 2023.11.10.566670 +Section: New Results}, + file = {Full Text PDF:/Users/aj/Zotero/storage/Y6RE4XU3/Yapp et al. - 2024 - Multiplexed 3D Analysis of Immune States and Niche.pdf:application/pdf}, } -@article{gerdes_highly_2013, - title = {Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue}, - volume = {110}, - url = {https://www.pnas.org/doi/10.1073/pnas.1300136110}, - doi = {10.1073/pnas.1300136110}, - abstract = {Limitations on the number of unique protein and {DNA} molecules that can be characterized microscopically in a single tissue specimen impede advances in understanding the biological basis of health and disease. Here we present a multiplexed fluorescence microscopy method ({MxIF}) for quantitative, single-cell, and subcellular characterization of multiple analytes in formalin-fixed paraffin-embedded tissue. Chemical inactivation of fluorescent dyes after each image acquisition round allows reuse of common dyes in iterative staining and imaging cycles. The mild inactivation chemistry is compatible with total and phosphoprotein detection, as well as {DNA} {FISH}. Accurate computational registration of sequential images is achieved by aligning nuclear counterstain-derived fiducial points. Individual cells, plasma membrane, cytoplasm, nucleus, tumor, and stromal regions are segmented to achieve cellular and subcellular quantification of multiplexed targets. In a comparison of pathologist scoring of diaminobenzidine staining of serial sections and automated {MxIF} scoring of a single section, human epidermal growth factor receptor 2, estrogen receptor, p53, and androgen receptor staining by diaminobenzidine and {MxIF} methods yielded similar results. Single-cell staining patterns of 61 protein antigens by {MxIF} in 747 colorectal cancer subjects reveals extensive tumor heterogeneity, and cluster analysis of divergent signaling through {ERK}1/2, S6 kinase 1, and 4E binding protein 1 provides insights into the spatial organization of mechanistic target of rapamycin and {MAPK} signal transduction. Our results suggest {MxIF} should be broadly applicable to problems in the fields of basic biological research, drug discovery and development, and clinical diagnostics.}, - pages = {11982--11987}, - number = {29}, - journaltitle = {Proceedings of the National Academy of Sciences}, - author = {Gerdes, Michael J. and Sevinsky, Christopher J. and Sood, Anup and Adak, Sudeshna and Bello, Musodiq O. and Bordwell, Alexander and Can, Ali and Corwin, Alex and Dinn, Sean and Filkins, Robert J. and Hollman, Denise and Kamath, Vidya and Kaanumalle, Sireesha and Kenny, Kevin and Larsen, Melinda and Lazare, Michael and Li, Qing and Lowes, Christina and {McCulloch}, Colin C. and {McDonough}, Elizabeth and Montalto, Michael C. and Pang, Zhengyu and Rittscher, Jens and Santamaria-Pang, Alberto and Sarachan, Brion D. and Seel, Maximilian L. and Seppo, Antti and Shaikh, Kashan and Sui, Yunxia and Zhang, Jingyu and Ginty, Fiona}, +@inproceedings{wang_spatial_2007, + title = {Spatial Latent Dirichlet Allocation}, + volume = {20}, + url = {https://papers.nips.cc/paper_files/paper/2007/hash/ec8956637a99787bd197eacd77acce5e-Abstract.html}, + abstract = {In recent years, the language model Latent Dirichlet Allocation ({LDA}), which clusters co-occurring words into topics, has been widely appled in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since {LDA} assumes that a document is a bag-of-words''. It is also critical to properly designwords'' and “documents” when using a language model to solve vision problems. In this paper, we propose a topic model Spatial Latent Dirichlet Allocation ({SLDA}), which better encodes spatial structure among visual words that are essential for solving many vision problems. The spatial information is not encoded in the value of visual words but in the design of documents. Instead of knowing the partition of words into documents {\textbackslash}textit\{a priori\}, the word-document assignment becomes a random hidden variable in {SLDA}. There is a generative procedure, where knowledge of spatial structure can be flexibly added as a prior, grouping visual words which are close in space into the same document. We use {SLDA} to discover objects from a collection of images, and show it achieves better performance than {LDA}.}, + booktitle = {Advances in Neural Information Processing Systems}, + publisher = {Curran Associates, Inc.}, + author = {Wang, Xiaogang and Grimson, Eric}, urldate = {2024-04-01}, - date = {2013-07-16}, - note = {Publisher: Proceedings of the National Academy of Sciences}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\57CUA4AI\\Gerdes et al. - 2013 - Highly multiplexed single-cell analysis of formali.pdf:application/pdf}, + date = {2007}, + file = {Full Text PDF:/Users/aj/Zotero/storage/L8IIW7FZ/Wang and Grimson - 2007 - Spatial Latent Dirichlet Allocation.pdf:application/pdf}, } -@article{giesen_highly_2014, - title = {Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry}, - volume = {11}, - rights = {2014 Springer Nature America, Inc.}, +@article{schapiro_mcmicro_2022, + title = {{MCMICRO}: a scalable, modular image-processing pipeline for multiplexed tissue imaging}, + volume = {19}, + rights = {2021 The Author(s)}, issn = {1548-7105}, - url = {https://www.nature.com/articles/nmeth.2869}, - doi = {10.1038/nmeth.2869}, - abstract = {This paper reports the use of mass cytometry on adherent cells and tissue samples for highly multiplexed imaging at subcellular resolution.}, - pages = {417--422}, - number = {4}, + url = {https://www.nature.com/articles/s41592-021-01308-y}, + doi = {10.1038/s41592-021-01308-y}, + shorttitle = {{MCMICRO}}, + abstract = {Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, {MCMICRO}, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of {MCMICRO} on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.}, + pages = {311--315}, + number = {3}, journaltitle = {Nature Methods}, shortjournal = {Nat Methods}, - author = {Giesen, Charlotte and Wang, Hao A. O. and Schapiro, Denis and Zivanovic, Nevena and Jacobs, Andrea and Hattendorf, Bodo and Schüffler, Peter J. and Grolimund, Daniel and Buhmann, Joachim M. and Brandt, Simone and Varga, Zsuzsanna and Wild, Peter J. and Günther, Detlef and Bodenmiller, Bernd}, + author = {Schapiro, Denis and Sokolov, Artem and Yapp, Clarence and Chen, Yu-An and Muhlich, Jeremy L. and Hess, Joshua and Creason, Allison L. and Nirmal, Ajit J. and Baker, Gregory J. and Nariya, Maulik K. and Lin, Jia-Ren and Maliga, Zoltan and Jacobson, Connor A. and Hodgman, Matthew W. and Ruokonen, Juha and Farhi, Samouil L. and Abbondanza, Domenic and {McKinley}, Eliot T. and Persson, Daniel and Betts, Courtney and Sivagnanam, Shamilene and Regev, Aviv and Goecks, Jeremy and Coffey, Robert J. and Coussens, Lisa M. and Santagata, Sandro and Sorger, Peter K.}, urldate = {2024-04-01}, - date = {2014-04}, + date = {2022-03}, langid = {english}, note = {Publisher: Nature Publishing Group}, - keywords = {Biotechnology, Systems biology}, + keywords = {Software, Image processing, Systems biology}, + file = {Full Text PDF:/Users/aj/Zotero/storage/4ASYQ4AL/Schapiro et al. - 2022 - MCMICRO a scalable, modular image-processing pipe.pdf:application/pdf}, } -@article{goltsev_deep_2018, - title = {Deep Profiling of Mouse Splenic Architecture with {CODEX} Multiplexed Imaging}, - volume = {174}, - issn = {1097-4172}, - doi = {10.1016/j.cell.2018.07.010}, - abstract = {A highly multiplexed cytometric imaging approach, termed co-detection by indexing ({CODEX}), is used here to create multiplexed datasets of normal and lupus ({MRL}/lpr) murine spleens. {CODEX} iteratively visualizes antibody binding events using {DNA} barcodes, fluorescent {dNTP} analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease ({MRL}/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.}, - pages = {968--981.e15}, - number = {4}, - journaltitle = {Cell}, - shortjournal = {Cell}, - author = {Goltsev, Yury and Samusik, Nikolay and Kennedy-Darling, Julia and Bhate, Salil and Hale, Matthew and Vazquez, Gustavo and Black, Sarah and Nolan, Garry P.}, - date = {2018-08-09}, - pmid = {30078711}, - pmcid = {PMC6086938}, - keywords = {Animals, Antibodies, autoimmunity, {CODEX}, Disease Models, Animal, Female, Image Processing, Computer-Assisted, immune tissue, Lupus Erythematosus, Systemic, Male, Mass Spectrometry, Mice, Mice, Inbred {MRL} lpr, microenvironment, multidimensional imaging, multiplexed imaging, niche, Oligonucleotide Probes, Spleen, tissue architecture}, - file = {Full Text:C\:\\Users\\aj\\Zotero\\storage\\S2CG5697\\Goltsev et al. - 2018 - Deep Profiling of Mouse Splenic Architecture with .pdf:application/pdf}, +@article{chiu_napari_2022, + title = {napari: a Python Multi-Dimensional Image Viewer Platform for the Research Community}, + volume = {28}, + issn = {1431-9276}, + url = {https://doi.org/10.1017/S1431927622006328}, + doi = {10.1017/S1431927622006328}, + shorttitle = {napari}, + pages = {1576--1577}, + issue = {S1}, + journaltitle = {Microscopy and Microanalysis}, + shortjournal = {Microscopy and Microanalysis}, + author = {Chiu, Chi-Li and Clack, Nathan and {the napari community}}, + urldate = {2024-04-01}, + date = {2022-08-01}, } -@article{gut_multiplexed_2018, - title = {Multiplexed protein maps link subcellular organization to cellular states}, - volume = {361}, - url = {https://www.science.org/doi/10.1126/science.aar7042}, - doi = {10.1126/science.aar7042}, - abstract = {Obtaining highly multiplexed protein measurements across multiple length scales has enormous potential for biomedicine. Here, we measured, by iterative indirect immunofluorescence imaging (4i), 40-plex protein readouts from biological samples at high-throughput from the millimeter to the nanometer scale. This approach simultaneously captures properties apparent at the population, cellular, and subcellular levels, including microenvironment, cell shape, and cell cycle state. It also captures the detailed morphology of organelles, cytoskeletal structures, nuclear subcompartments, and the fate of signaling receptors in thousands of single cells in situ. We used computer vision and systems biology approaches to achieve unsupervised comprehensive quantification of protein subcompartmentalization within various multicellular, cellular, and pharmacological contexts. Thus, highly multiplexed subcellular protein maps can be used to identify functionally relevant single-cell states.}, - pages = {eaar7042}, - number = {6401}, - journaltitle = {Science}, - author = {Gut, Gabriele and Herrmann, Markus D. and Pelkmans, Lucas}, +@misc{nirmal_cell_2023, + title = {Cell Spotter ({CSPOT}): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images}, + rights = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, + url = {https://www.biorxiv.org/content/10.1101/2023.11.15.567196v1}, + doi = {10.1101/2023.11.15.567196}, + shorttitle = {Cell Spotter ({CSPOT})}, + abstract = {Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or {CSPOT}) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. {CSPOT} is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of {CSPOT} can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.}, + publisher = {{bioRxiv}}, + author = {Nirmal, Ajit J. and Yapp, Clarence and Santagata, Sandro and Sorger, Peter K.}, urldate = {2024-04-01}, - date = {2018-08-03}, - note = {Publisher: American Association for the Advancement of Science}, + date = {2023-11-17}, + langid = {english}, + note = {Pages: 2023.11.15.567196 +Section: New Results}, + file = {Full Text PDF:/Users/aj/Zotero/storage/P94L8WEI/Nirmal et al. - 2023 - Cell Spotter (CSPOT) A machine-learning approach .pdf:application/pdf}, } -@article{tsujikawa_quantitative_2017, - title = {Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis}, - volume = {19}, - issn = {2211-1247}, - doi = {10.1016/j.celrep.2017.03.037}, - abstract = {Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant {GVAX} vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of {CD}8+ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.}, - pages = {203--217}, - number = {1}, - journaltitle = {Cell Reports}, - shortjournal = {Cell Rep}, - author = {Tsujikawa, Takahiro and Kumar, Sushil and Borkar, Rohan N. and Azimi, Vahid and Thibault, Guillaume and Chang, Young Hwan and Balter, Ariel and Kawashima, Rie and Choe, Gina and Sauer, David and El Rassi, Edward and Clayburgh, Daniel R. and Kulesz-Martin, Molly F. and Lutz, Eric R. and Zheng, Lei and Jaffee, Elizabeth M. and Leyshock, Patrick and Margolin, Adam A. and Mori, Motomi and Gray, Joe W. and Flint, Paul W. and Coussens, Lisa M.}, - date = {2017-04-04}, - pmid = {28380359}, - pmcid = {PMC5564306}, - keywords = {Aged, Aged, 80 and over, Biomarkers, Tumor, cancer immunology, Carcinoma, Squamous Cell, Cohort Studies, digital pathology, Female, head and neck cancer, Head and Neck Neoplasms, Humans, image cytometry, Image Cytometry, Image Processing, Computer-Assisted, immunohistochemistry, Immunohistochemistry, Male, Middle Aged, Monitoring, Immunologic, multiplex, pancreatic cancer, Prognosis, Statistics, Nonparametric, Tissue Array Analysis, tissue biomarker}, - file = {Accepted Version:C\:\\Users\\aj\\Zotero\\storage\\B47EURAE\\Tsujikawa et al. - 2017 - Quantitative Multiplex Immunohistochemistry Reveal.pdf:application/pdf}, +@misc{baker_quality_2024, + title = {Quality Control for Single Cell Analysis of High-plex Tissue Profiles using {CyLinter}}, + rights = {© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, + url = {https://www.biorxiv.org/content/10.1101/2023.11.01.565120v2}, + doi = {10.1101/2023.11.01.565120}, + abstract = {Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103–107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, {CyLinter}, that identifies and removes data associated with imaging artefacts. {CyLinter} greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.}, + publisher = {{bioRxiv}}, + author = {Baker, Gregory J. and Novikov, Edward and Zhao, Ziyuan and Vallius, Tuulia and Davis, Janae A. and Lin, Jia-Ren and Muhlich, Jeremy L. and Mittendorf, Elizabeth A. and Santagata, Sandro and Guerriero, Jennifer L. and Sorger, Peter K.}, + urldate = {2024-04-01}, + date = {2024-03-22}, + langid = {english}, + note = {Pages: 2023.11.01.565120 +Section: New Results}, + file = {Full Text PDF:/Users/aj/Zotero/storage/RS5L77TP/Baker et al. - 2024 - Quality Control for Single Cell Analysis of High-p.pdf:application/pdf}, } @article{lin_highly_2018, @@ -157,155 +161,1186 @@ @article{lin_highly_2018 date = {2018-07-11}, pmid = {29993362}, pmcid = {PMC6075866}, - keywords = {Antigens, Neoplasm, cancer biology, computational biology, human, Humans, Immunologic Factors, immunopathology, Microscopy, Fluorescence, multiplexed imaging, Neoplasms, Signal Transduction, single-cell method, systems biology}, - file = {Full Text:C\:\\Users\\aj\\Zotero\\storage\\D3IYT75Z\\Lin et al. - 2018 - Highly multiplexed immunofluorescence imaging of h.pdf:application/pdf}, + keywords = {computational biology, multiplexed imaging, systems biology, Humans, Neoplasms, Immunologic Factors, Microscopy, Fluorescence, Signal Transduction, Antigens, Neoplasm, cancer biology, human, immunopathology, single-cell method}, + file = {Full Text:/Users/aj/Zotero/storage/D3IYT75Z/Lin et al. - 2018 - Highly multiplexed immunofluorescence imaging of h.pdf:application/pdf}, } -@misc{baker_quality_2024, - title = {Quality Control for Single Cell Analysis of High-plex Tissue Profiles using {CyLinter}}, - rights = {© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, - url = {https://www.biorxiv.org/content/10.1101/2023.11.01.565120v2}, - doi = {10.1101/2023.11.01.565120}, - abstract = {Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103–107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, {CyLinter}, that identifies and removes data associated with imaging artefacts. {CyLinter} greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.}, - publisher = {{bioRxiv}}, - author = {Baker, Gregory J. and Novikov, Edward and Zhao, Ziyuan and Vallius, Tuulia and Davis, Janae A. and Lin, Jia-Ren and Muhlich, Jeremy L. and Mittendorf, Elizabeth A. and Santagata, Sandro and Guerriero, Jennifer L. and Sorger, Peter K.}, +@article{tsujikawa_quantitative_2017, + title = {Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis}, + volume = {19}, + issn = {2211-1247}, + doi = {10.1016/j.celrep.2017.03.037}, + abstract = {Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant {GVAX} vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of {CD}8+ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.}, + pages = {203--217}, + number = {1}, + journaltitle = {Cell Reports}, + shortjournal = {Cell Rep}, + author = {Tsujikawa, Takahiro and Kumar, Sushil and Borkar, Rohan N. and Azimi, Vahid and Thibault, Guillaume and Chang, Young Hwan and Balter, Ariel and Kawashima, Rie and Choe, Gina and Sauer, David and El Rassi, Edward and Clayburgh, Daniel R. and Kulesz-Martin, Molly F. and Lutz, Eric R. and Zheng, Lei and Jaffee, Elizabeth M. and Leyshock, Patrick and Margolin, Adam A. and Mori, Motomi and Gray, Joe W. and Flint, Paul W. and Coussens, Lisa M.}, + date = {2017-04-04}, + pmid = {28380359}, + pmcid = {PMC5564306}, + keywords = {Humans, Image Processing, Computer-Assisted, Cohort Studies, Head and Neck Neoplasms, Female, Aged, Male, Middle Aged, Biomarkers, Tumor, Immunohistochemistry, Prognosis, Tissue Array Analysis, Aged, 80 and over, pancreatic cancer, Carcinoma, Squamous Cell, Image Cytometry, immunohistochemistry, Statistics, Nonparametric, cancer immunology, digital pathology, head and neck cancer, image cytometry, Monitoring, Immunologic, multiplex, tissue biomarker}, + file = {Accepted Version:/Users/aj/Zotero/storage/B47EURAE/Tsujikawa et al. - 2017 - Quantitative Multiplex Immunohistochemistry Reveal.pdf:application/pdf}, +} + +@article{gut_multiplexed_2018, + title = {Multiplexed protein maps link subcellular organization to cellular states}, + volume = {361}, + url = {https://www.science.org/doi/10.1126/science.aar7042}, + doi = {10.1126/science.aar7042}, + abstract = {Obtaining highly multiplexed protein measurements across multiple length scales has enormous potential for biomedicine. Here, we measured, by iterative indirect immunofluorescence imaging (4i), 40-plex protein readouts from biological samples at high-throughput from the millimeter to the nanometer scale. This approach simultaneously captures properties apparent at the population, cellular, and subcellular levels, including microenvironment, cell shape, and cell cycle state. It also captures the detailed morphology of organelles, cytoskeletal structures, nuclear subcompartments, and the fate of signaling receptors in thousands of single cells in situ. We used computer vision and systems biology approaches to achieve unsupervised comprehensive quantification of protein subcompartmentalization within various multicellular, cellular, and pharmacological contexts. Thus, highly multiplexed subcellular protein maps can be used to identify functionally relevant single-cell states.}, + pages = {eaar7042}, + number = {6401}, + journaltitle = {Science}, + author = {Gut, Gabriele and Herrmann, Markus D. and Pelkmans, Lucas}, urldate = {2024-04-01}, - date = {2024-03-22}, - langid = {english}, - note = {Pages: 2023.11.01.565120 -Section: New Results}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\RS5L77TP\\Baker et al. - 2024 - Quality Control for Single Cell Analysis of High-p.pdf:application/pdf}, + date = {2018-08-03}, + note = {Publisher: American Association for the Advancement of Science}, } -@misc{nirmal_cell_2023, - title = {Cell Spotter ({CSPOT}): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images}, - rights = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, - url = {https://www.biorxiv.org/content/10.1101/2023.11.15.567196v1}, - doi = {10.1101/2023.11.15.567196}, - shorttitle = {Cell Spotter ({CSPOT})}, - abstract = {Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or {CSPOT}) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. {CSPOT} is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of {CSPOT} can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.}, - publisher = {{bioRxiv}}, - author = {Nirmal, Ajit J. and Yapp, Clarence and Santagata, Sandro and Sorger, Peter K.}, - urldate = {2024-04-01}, - date = {2023-11-17}, - langid = {english}, - note = {Pages: 2023.11.15.567196 -Section: New Results}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\P94L8WEI\\Nirmal et al. - 2023 - Cell Spotter (CSPOT) A machine-learning approach .pdf:application/pdf}, -} - -@article{chiu_napari_2022, - title = {napari: a Python Multi-Dimensional Image Viewer Platform for the Research Community}, - volume = {28}, - issn = {1431-9276}, - url = {https://doi.org/10.1017/S1431927622006328}, - doi = {10.1017/S1431927622006328}, - shorttitle = {napari}, - pages = {1576--1577}, - issue = {S1}, - journaltitle = {Microscopy and Microanalysis}, - shortjournal = {Microscopy and Microanalysis}, - author = {Chiu, Chi-Li and Clack, Nathan and {the napari community}}, - urldate = {2024-04-01}, - date = {2022-08-01}, +@article{goltsev_deep_2018, + title = {Deep Profiling of Mouse Splenic Architecture with {CODEX} Multiplexed Imaging}, + volume = {174}, + issn = {1097-4172}, + doi = {10.1016/j.cell.2018.07.010}, + abstract = {A highly multiplexed cytometric imaging approach, termed co-detection by indexing ({CODEX}), is used here to create multiplexed datasets of normal and lupus ({MRL}/lpr) murine spleens. {CODEX} iteratively visualizes antibody binding events using {DNA} barcodes, fluorescent {dNTP} analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease ({MRL}/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.}, + pages = {968--981.e15}, + number = {4}, + journaltitle = {Cell}, + shortjournal = {Cell}, + author = {Goltsev, Yury and Samusik, Nikolay and Kennedy-Darling, Julia and Bhate, Salil and Hale, Matthew and Vazquez, Gustavo and Black, Sarah and Nolan, Garry P.}, + date = {2018-08-09}, + pmid = {30078711}, + pmcid = {PMC6086938}, + keywords = {multiplexed imaging, Image Processing, Computer-Assisted, Antibodies, Female, Animals, Mice, Male, Mass Spectrometry, Disease Models, Animal, Spleen, microenvironment, autoimmunity, {CODEX}, immune tissue, Lupus Erythematosus, Systemic, Mice, Inbred {MRL} lpr, multidimensional imaging, niche, Oligonucleotide Probes, tissue architecture}, + file = {Full Text:/Users/aj/Zotero/storage/S2CG5697/Goltsev et al. - 2018 - Deep Profiling of Mouse Splenic Architecture with .pdf:application/pdf}, } -@article{schapiro_mcmicro_2022, - title = {{MCMICRO}: a scalable, modular image-processing pipeline for multiplexed tissue imaging}, - volume = {19}, - rights = {2021 The Author(s)}, +@article{giesen_highly_2014, + title = {Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry}, + volume = {11}, + rights = {2014 Springer Nature America, Inc.}, issn = {1548-7105}, - url = {https://www.nature.com/articles/s41592-021-01308-y}, - doi = {10.1038/s41592-021-01308-y}, - shorttitle = {{MCMICRO}}, - abstract = {Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, {MCMICRO}, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of {MCMICRO} on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.}, - pages = {311--315}, - number = {3}, + url = {https://www.nature.com/articles/nmeth.2869}, + doi = {10.1038/nmeth.2869}, + abstract = {This paper reports the use of mass cytometry on adherent cells and tissue samples for highly multiplexed imaging at subcellular resolution.}, + pages = {417--422}, + number = {4}, journaltitle = {Nature Methods}, shortjournal = {Nat Methods}, - author = {Schapiro, Denis and Sokolov, Artem and Yapp, Clarence and Chen, Yu-An and Muhlich, Jeremy L. and Hess, Joshua and Creason, Allison L. and Nirmal, Ajit J. and Baker, Gregory J. and Nariya, Maulik K. and Lin, Jia-Ren and Maliga, Zoltan and Jacobson, Connor A. and Hodgman, Matthew W. and Ruokonen, Juha and Farhi, Samouil L. and Abbondanza, Domenic and {McKinley}, Eliot T. and Persson, Daniel and Betts, Courtney and Sivagnanam, Shamilene and Regev, Aviv and Goecks, Jeremy and Coffey, Robert J. and Coussens, Lisa M. and Santagata, Sandro and Sorger, Peter K.}, + author = {Giesen, Charlotte and Wang, Hao A. O. and Schapiro, Denis and Zivanovic, Nevena and Jacobs, Andrea and Hattendorf, Bodo and Schüffler, Peter J. and Grolimund, Daniel and Buhmann, Joachim M. and Brandt, Simone and Varga, Zsuzsanna and Wild, Peter J. and Günther, Detlef and Bodenmiller, Bernd}, urldate = {2024-04-01}, - date = {2022-03}, + date = {2014-04}, langid = {english}, note = {Publisher: Nature Publishing Group}, - keywords = {Image processing, Software, Systems biology}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\4ASYQ4AL\\Schapiro et al. - 2022 - MCMICRO a scalable, modular image-processing pipe.pdf:application/pdf}, + keywords = {Systems biology, Biotechnology}, } -@inproceedings{wang_spatial_2007, - title = {Spatial Latent Dirichlet Allocation}, - volume = {20}, - url = {https://papers.nips.cc/paper_files/paper/2007/hash/ec8956637a99787bd197eacd77acce5e-Abstract.html}, - abstract = {In recent years, the language model Latent Dirichlet Allocation ({LDA}), which clusters co-occurring words into topics, has been widely appled in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since {LDA} assumes that a document is a bag-of-words''. It is also critical to properly designwords'' and “documents” when using a language model to solve vision problems. In this paper, we propose a topic model Spatial Latent Dirichlet Allocation ({SLDA}), which better encodes spatial structure among visual words that are essential for solving many vision problems. The spatial information is not encoded in the value of visual words but in the design of documents. Instead of knowing the partition of words into documents {\textbackslash}textit\{a priori\}, the word-document assignment becomes a random hidden variable in {SLDA}. There is a generative procedure, where knowledge of spatial structure can be flexibly added as a prior, grouping visual words which are close in space into the same document. We use {SLDA} to discover objects from a collection of images, and show it achieves better performance than {LDA}.}, - booktitle = {Advances in Neural Information Processing Systems}, - publisher = {Curran Associates, Inc.}, - author = {Wang, Xiaogang and Grimson, Eric}, +@article{gerdes_highly_2013, + title = {Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue}, + volume = {110}, + url = {https://www.pnas.org/doi/10.1073/pnas.1300136110}, + doi = {10.1073/pnas.1300136110}, + abstract = {Limitations on the number of unique protein and {DNA} molecules that can be characterized microscopically in a single tissue specimen impede advances in understanding the biological basis of health and disease. Here we present a multiplexed fluorescence microscopy method ({MxIF}) for quantitative, single-cell, and subcellular characterization of multiple analytes in formalin-fixed paraffin-embedded tissue. Chemical inactivation of fluorescent dyes after each image acquisition round allows reuse of common dyes in iterative staining and imaging cycles. The mild inactivation chemistry is compatible with total and phosphoprotein detection, as well as {DNA} {FISH}. Accurate computational registration of sequential images is achieved by aligning nuclear counterstain-derived fiducial points. Individual cells, plasma membrane, cytoplasm, nucleus, tumor, and stromal regions are segmented to achieve cellular and subcellular quantification of multiplexed targets. In a comparison of pathologist scoring of diaminobenzidine staining of serial sections and automated {MxIF} scoring of a single section, human epidermal growth factor receptor 2, estrogen receptor, p53, and androgen receptor staining by diaminobenzidine and {MxIF} methods yielded similar results. Single-cell staining patterns of 61 protein antigens by {MxIF} in 747 colorectal cancer subjects reveals extensive tumor heterogeneity, and cluster analysis of divergent signaling through {ERK}1/2, S6 kinase 1, and 4E binding protein 1 provides insights into the spatial organization of mechanistic target of rapamycin and {MAPK} signal transduction. Our results suggest {MxIF} should be broadly applicable to problems in the fields of basic biological research, drug discovery and development, and clinical diagnostics.}, + pages = {11982--11987}, + number = {29}, + journaltitle = {Proceedings of the National Academy of Sciences}, + author = {Gerdes, Michael J. and Sevinsky, Christopher J. and Sood, Anup and Adak, Sudeshna and Bello, Musodiq O. and Bordwell, Alexander and Can, Ali and Corwin, Alex and Dinn, Sean and Filkins, Robert J. and Hollman, Denise and Kamath, Vidya and Kaanumalle, Sireesha and Kenny, Kevin and Larsen, Melinda and Lazare, Michael and Li, Qing and Lowes, Christina and {McCulloch}, Colin C. and {McDonough}, Elizabeth and Montalto, Michael C. and Pang, Zhengyu and Rittscher, Jens and Santamaria-Pang, Alberto and Sarachan, Brion D. and Seel, Maximilian L. and Seppo, Antti and Shaikh, Kashan and Sui, Yunxia and Zhang, Jingyu and Ginty, Fiona}, urldate = {2024-04-01}, - date = {2007}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\L8IIW7FZ\\Wang and Grimson - 2007 - Spatial Latent Dirichlet Allocation.pdf:application/pdf}, + date = {2013-07-16}, + note = {Publisher: Proceedings of the National Academy of Sciences}, + file = {Full Text PDF:/Users/aj/Zotero/storage/57CUA4AI/Gerdes et al. - 2013 - Highly multiplexed single-cell analysis of formali.pdf:application/pdf}, } -@misc{yapp_multiplexed_2024, - title = {Multiplexed 3D Analysis of Immune States and Niches in Human Tissue}, - rights = {© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, - url = {https://www.biorxiv.org/content/10.1101/2023.11.10.566670v3}, - doi = {10.1101/2023.11.10.566670}, - abstract = {Tissue homeostasis and the emergence of disease are controlled by changes in the proportions of resident and recruited cells, their organization into cellular neighbourhoods, and their interactions with acellular tissue components. Highly multiplexed tissue profiling (spatial omics) makes it possible to study this microenvironment in situ, usually in 4-5 micron thick sections (the standard histopathology format). Microscopy-based tissue profiling is commonly performed at a resolution sufficient to determine cell types but not to detect subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking. Here we describe a high-resolution 3D imaging approach able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. This approach combines cyclic immunofluorescence ({CyCIF}) imaging of over 50 markers with confocal microscopy of archival human tissue thick enough (30-40 microns) to fully encompass two or more layers of intact cells. 3D imaging of entire cell volumes substantially improves the accuracy of cell phenotyping and allows cell proximity to be scored using plasma membrane apposition, not just nuclear position. In pre-invasive melanoma in situ, precise phenotyping shows that adjacent melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. In this and metastatic melanoma, mature and precursor T cells engage in an unexpectedly diverse array of juxtracrine and membrane-membrane interactions as well as looser neighbourhood associations whose morphologies reveal functional states. These data provide new insight into the transitions occurring during early tumour formation and immunoediting and demonstrate the potential for phenotyping of tissues at a level of detail previously restricted to cultured cells and organoids.}, - publisher = {{bioRxiv}}, - author = {Yapp, Clarence and Nirmal, Ajit J. and Zhou, Felix Yuran and Maliga, Zoltan and Tefft, Juliann and Llopis, Paula Montero and Murphy, George F. and Lian, Christine and Danuser, Gaudenz and Santagata, Sandro and Sorger, Peter Karl}, +@article{angelo_multiplexed_2014, + title = {Multiplexed ion beam imaging of human breast tumors}, + volume = {20}, + rights = {2014 Springer Nature America, Inc.}, + issn = {1546-170X}, + url = {https://www.nature.com/articles/nm.3488}, + doi = {10.1038/nm.3488}, + abstract = {The work of Michael Angelo and colleagues uses multiplexed ion beam imaging ({MIBI}) to localize and visualize protein expression in a manner analogous to immunohistochemistry ({IHC}) while circumventing some of the limitations of conventional {IHC} with clinical samples. {MIBI} uses secondary ion mass spectrometry to image antibodies tagged with isotopically pure elemental metal reporters, expanding the number of targets that can be analyzed simultaneously to about 100. The approach, used here to image breast tumor tissue sections, offers over a five-log dynamic range and compatibility with standard formalin-fixed, paraffin-embedded tissue sections.}, + pages = {436--442}, + number = {4}, + journaltitle = {Nature Medicine}, + shortjournal = {Nat Med}, + author = {Angelo, Michael and Bendall, Sean C. and Finck, Rachel and Hale, Matthew B. and Hitzman, Chuck and Borowsky, Alexander D. and Levenson, Richard M. and Lowe, John B. and Liu, Scot D. and Zhao, Shuchun and Natkunam, Yasodha and Nolan, Garry P.}, urldate = {2024-04-01}, - date = {2024-03-28}, + date = {2014-04}, langid = {english}, - note = {Pages: 2023.11.10.566670 -Section: New Results}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\Y6RE4XU3\\Yapp et al. - 2024 - Multiplexed 3D Analysis of Immune States and Niche.pdf:application/pdf}, + note = {Publisher: Nature Publishing Group}, + keywords = {Imaging, Immunohistochemistry, Computational biology and bioinformatics}, + file = {Full Text PDF:/Users/aj/Zotero/storage/3BG8JWWH/Angelo et al. - 2014 - Multiplexed ion beam imaging of human breast tumor.pdf:application/pdf}, } -@article{nirmal_spatial_2022, - title = {The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution}, +@article{liu_analysis_2022, + title = {Analysis and Visualization of Spatial Transcriptomic Data}, volume = {12}, - issn = {2159-8274}, - url = {https://doi.org/10.1158/2159-8290.CD-21-1357}, - doi = {10.1158/2159-8290.CD-21-1357}, - abstract = {Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor–stromal boundary. This environment is established by cytokine gradients that promote expression of {MHC}-{II} and {IDO}1, and by {PD}1–{PDL}1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen.The reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classic histopathology, spatial profiling of proteins and {mRNA} reveals recurrent morphologic and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell–cell contact.This article is highlighted in the In This Issue feature, p. 1397}, - pages = {1518--1541}, - number = {6}, - journaltitle = {Cancer Discovery}, - shortjournal = {Cancer Discovery}, - author = {Nirmal, Ajit J. and Maliga, Zoltan and Vallius, Tuulia and Quattrochi, Brian and Chen, Alyce A. and Jacobson, Connor A. and Pelletier, Roxanne J. and Yapp, Clarence and Arias-Camison, Raquel and Chen, Yu-An and Lian, Christine G. and Murphy, George F. and Santagata, Sandro and Sorger, Peter K.}, + issn = {1664-8021}, + url = {https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.785290/full}, + doi = {10.3389/fgene.2021.785290}, + abstract = {{\textless}p{\textgreater}Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. {RNA} molecules are measured by {\textless}italic{\textgreater}in situ{\textless}/italic{\textgreater} sequencing, {\textless}italic{\textgreater}in situ{\textless}/italic{\textgreater} hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.{\textless}/p{\textgreater}}, + journaltitle = {Frontiers in Genetics}, + shortjournal = {Front. Genet.}, + author = {Liu, Boxiang and Li, Yanjun and Zhang, Liang}, urldate = {2024-04-01}, - date = {2022-06-02}, - file = {Full Text PDF:C\:\\Users\\aj\\Zotero\\storage\\NPFVJ8J8\\Nirmal et al. - 2022 - The Spatial Landscape of Progression and Immunoedi.pdf:application/pdf;Snapshot:C\:\\Users\\aj\\Zotero\\storage\\9VX8ZHYQ\\The-Spatial-Landscape-of-Progression-and.html:text/html}, + date = {2022-01-27}, + note = {Publisher: Frontiers}, + keywords = {visualization, Cell-to-cell interaction, clustering, Single-cell {RNA}-seq ({scRNA}-seq), Spatial expression pattern, Spatial transcriptomics}, + file = {Full Text:/Users/aj/Zotero/storage/KFMS2MHW/Liu et al. - 2022 - Analysis and Visualization of Spatial Transcriptom.pdf:application/pdf}, } -@article{gaglia_lymphocyte_2023, - title = {Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma}, - volume = {41}, - issn = {1878-3686}, - doi = {10.1016/j.ccell.2023.03.015}, - abstract = {Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. {CXCR}3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored {TCF}1+ {PD}-1+ progenitor {CD}8+ T cells involved in responses to immune checkpoint blockade ({ICB}) therapy. Upon treatment of mice with {ICB} or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic {CD}8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of {CD}8+ T cell anti-tumor responses.}, - pages = {871--886.e10}, - number = {5}, - journaltitle = {Cancer Cell}, - shortjournal = {Cancer Cell}, - author = {Gaglia, Giorgio and Burger, Megan L. and Ritch, Cecily C. and Rammos, Danae and Dai, Yang and Crossland, Grace E. and Tavana, Sara Z. and Warchol, Simon and Jaeger, Alex M. and Naranjo, Santiago and Coy, Shannon and Nirmal, Ajit J. and Krueger, Robert and Lin, Jia-Ren and Pfister, Hanspeter and Sorger, Peter K. and Jacks, Tyler and Santagata, Sandro}, - date = {2023-05-08}, - pmid = {37059105}, - pmcid = {PMC10193529}, - keywords = {Adenocarcinoma of Lung, Animals, cancer vaccines, {CD}8-Positive T-Lymphocytes, computational biology, {CyCIF}, Humans, Immunity, immunotherapy, Immunotherapy, lung adenocarcinoma, Lung Neoplasms, Mice, multimodal data integration, multiplexed imaging, spatial biology, spatial profiling, systems biology}, - file = {Submitted Version:C\:\\Users\\aj\\Zotero\\storage\\A4Y42LBZ\\Gaglia et al. - 2023 - Lymphocyte networks are dynamic cellular communiti.pdf:application/pdf}, +@article{noauthor_catching_2022, + title = {Catching up with multiplexed tissue imaging}, + volume = {19}, + rights = {2022 Springer Nature America, Inc.}, + issn = {1548-7105}, + url = {https://www.nature.com/articles/s41592-022-01428-z}, + doi = {10.1038/s41592-022-01428-z}, + abstract = {Highly multiplexed tissue imaging continues to show its power for biomedical discovery. In this issue, we publish tools and guidance for implementing this class of methods and reporting subsequent results.}, + pages = {259--259}, + number = {3}, + journaltitle = {Nature Methods}, + shortjournal = {Nat Methods}, + urldate = {2024-04-01}, + date = {2022-03}, + langid = {english}, + note = {Publisher: Nature Publishing Group}, + keywords = {Scientific community, Fluorescence imaging, Histology, Immunocytochemistry}, + file = {Full Text PDF:/Users/aj/Zotero/storage/X8ANJRMA/2022 - Catching up with multiplexed tissue imaging.pdf:application/pdf}, } -@article{maliga_immune_2024, - title = {Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade Using Tissue Cyclic Immunofluorescence: A Pilot Study}, - issn = {1523-1747}, - doi = {10.1016/j.jid.2024.01.024}, - shorttitle = {Immune Profiling of Dermatologic Adverse Events from Checkpoint Blockade Using Tissue Cyclic Immunofluorescence}, - pages = {S0022--202X(24)00107--6}, - journaltitle = {The Journal of Investigative Dermatology}, - shortjournal = {J Invest Dermatol}, - author = {Maliga, Zoltan and Kim, Daniel Y. and Bui, Ai-Tram N. and Lin, Jia-Ren and Dewan, Anna K. and Jadeja, Saagar and Murphy, George F. and Nirmal, Ajit J. and Lian, Christine G. and Sorger, Peter K. and {LeBoeuf}, Nicole R.}, - date = {2024-02-14}, - pmid = {38360200}, - keywords = {Cutaneous adverse events, Immune checkpoint blockade, Immune-related adverse events, Immunotherapy, Tissue cyclic immunofluorescence}, +@software{ahlers_napari_2023, + title = {napari: a multi-dimensional image viewer for Python}, + url = {https://zenodo.org/records/8115575}, + shorttitle = {napari}, + abstract = {napari 0.4.18 +We're happy to announce the release of napari 0.4.18! +napari is a fast, interactive, multi-dimensional image viewer for Python. +It's designed for browsing, annotating, and analyzing large multi-dimensional +images. It's built on top of Qt (for the {GUI}), vispy (for performant {GPU}-based +rendering), and the scientific Python stack (numpy, scipy). + +This is primarily a bug-fix release, addressing many issues from 0.4.17 (see +"Bug Fixes", below). However, it also contains some performance improvements and +several exciting new features (see "Highlights"), so read on below! + +For more information, examples, and documentation, please visit our website: + +https://napari.org +Highlights + + + +Drawing polygons in the Shapes layer can now be done much faster with +the new lasso tool (napari/napari/\#5555) + +Surface layers now support textures and vertex colors, allowing a whole new +type of dataset to be visualised in napari. Have a look at +surface\_multi\_texture.py and surface\_texture\_and\_colors.py in the +examples directory for some pretty demos! (napari/napari/\#5642) + +Previously, navigating an image required switching out of whatever drawing +mode you might have been using and going back to pan/zoom mode. Now you can use +the mouse wheel to zoom in and out in any mode. (napari/napari/\#5701) + +Painting labels is now much, much faster (achieving 60fps even on an 8k x 8k +image) (napari/napari/\#5723 and +napari/napari/\#5732) + +Vectors layers can now be displayed with two different styles of arrowheads, +instead of just plain lines. This removes a longstanding limitation of the +vectors layer! (napari/napari/\#5740) + +New Features + + + +Overlays 2.0 (napari/napari/\#4894) + +expose custom image interpolation kernels (napari/napari/\#5130) + +Add user agent environment variable for pip installations (napari/napari/\#5135) + +Add option to check if plugin try to set viewer attr outside main thread (napari/napari/\#5195) + +Set selection color for {QListView} item. (napari/napari/\#5202) + +Add warning about set private attr when using proxy (napari/napari/\#5209) + +Shapes interpolation (napari/napari/\#5334) + +Add dask settings to preferences (napari/napari/\#5490) + +Add lasso tool for faster drawing of polygonal Shapes (napari/napari/\#5555) + +Feature: support for textures and vertex colors on Surface layers (napari/napari/\#5642) + +Back point selection with a psygnal Selection (napari/napari/\#5691) + +Zooming with the mouse wheel in any mode (napari/napari/\#5701) + +Add cancellation functionality to progress (napari/napari/\#5728) + +Add arrow display styles to Vectors layer (napari/napari/\#5740) + +Improvements + + + +Set keyboard focus on console when opened (napari/napari/\#5208) + +Push variables to console when instantiated (napari/napari/\#5210) + +Tracks layer creation performance improvement (napari/napari/\#5303) + +{PERF}: Event emissions and perf regression. (napari/napari/\#5307) + +Much faster {FormatStringEncoding} (napari/napari/\#5315) + +Add parent when creating layer context menu to inherit application theme and add style entry for disabled widgets and menus (napari/napari/\#5381) + +Add correct enablement kwarg to Split Stack action, Convert data type submenu and Projections submenu (napari/napari/\#5437) + +Apply disabled widgets style only for menus and set menus styles for {QModelMenu} and {QMenu} instances (napari/napari/\#5446) + +Add disabled style rule for {QComboBox} following the one for {QPushButton} (napari/napari/\#5469) + +Allow layers control section to resize to contents (napari/napari/\#5474) + +Allow to use Optional annotation in function return type for magicgui functions (napari/napari/\#5595) + +Skip equality comparisons in {EventedModel} when unnecessary (napari/napari/\#5615) + +Bugfix: improve layout of Preferences {\textgreater} Shortcuts tables (napari/napari/\#5679) + +Improve preferences genration (napari/napari/\#5696) + +Add dev example for adding custom overlays. (napari/napari/\#5719) + +Disable buffer swapping (napari/napari/\#5741) + +Remove max brush size from increase brush size keybinding (napari/napari/\#5761) + +Explicitly list valid layer names in types (napari/napari/\#5823) + +Sort npe1 widget contributions (napari/napari/\#5865) + +feat: add since\_version argument of rename\_argument decorator (napari/napari/\#5910) + +Emit extra information with layer.events.data (napari/napari/\#5967) + +Performance + + + +Return early when no slicing needed (napari/napari/\#5239) + +Tracks layer creation performance improvement (napari/napari/\#5303) + +{PERF}: Event emissions and perf regression. (napari/napari/\#5307) + +Much faster {FormatStringEncoding} (napari/napari/\#5315) + +Fix inefficient label mapping in direct color mode (10-20x speedup) (napari/napari/\#5723) + +Efficient labels mapping for drawing in Labels (60 {FPS} even with 8000x8000 images) (napari/napari/\#5732) + +Disable buffer swapping (napari/napari/\#5741) + +Bug Fixes + + + +Warn instead of failing on empty or invalid alt-text (napari/napari/\#4505) + +Fix display of order and scale combinations (napari/napari/\#5004) + +Enforce that contrast limits must be increasing (napari/napari/\#5036) + +Bugfix: Move Window menu to be before Help (napari/napari/\#5093) + +Add extra garbage collection for some viewer tests (napari/napari/\#5108) + +Connect image to plane events and expose them (napari/napari/\#5131) + +Workaround for discover themes from plugins (napari/napari/\#5150) + +Add missed dialogs to qtbot in test\_qt\_notifications to prevent segfaults (napari/napari/\#5171) + +{DOC} Update docstring of add\_dock\_widget \& \_add\_viewer\_dock\_widget (napari/napari/\#5173) + +Fix unsortable features (napari/napari/\#5186) + +Avoid possible divide-by-zero in Vectors layer thumbnail update (napari/napari/\#5192) + +Disable napari-console button when launched from jupyter (napari/napari/\#5213) + +Volume rendering updates for isosurface and attenuated {MIP} (napari/napari/\#5215) + +Return early when no slicing needed (napari/napari/\#5239) + +Check strictly increasing values when clipping contrast limits to a new range (napari/napari/\#5258) + +{UI} Bugfix: Make disabled {QPushButton} more distinct (napari/napari/\#5262) + +Respect background color when calculating scale bar color (napari/napari/\#5270) + +Fix circular import in \_vispy module (napari/napari/\#5276) + +Use only data dimensions for cord in status bar (napari/napari/\#5283) + +Prevent obsolete reports about failure of cleaning viewer instances (napari/napari/\#5317) + +Add scikit-image[data] to install\_requires, because it's required by builtins (napari/napari/\#5329) + +Fix repeating close dialog on {macOS} and qt 5.12 (napari/napari/\#5337) + +Disable napari-console if napari launched from vanilla python {REPL} (napari/napari/\#5350) + +For npe2 plugin, use manifest display\_name for File {\textgreater} Open Samples (napari/napari/\#5351) + +Bugfix plugin display\_name use (File {\textgreater} Open Sample, Plugin menus) (napari/napari/\#5366) + +Fix editing shape data above 2 dimensions (napari/napari/\#5383) + +Fix test keybinding for layer actions (napari/napari/\#5406) + +fix theme id not being used correctly (napari/napari/\#5412) + +Clarify layer's editable property and separate interaction with visible property (napari/napari/\#5413) + +Fix theme reference to get image for success\_label style (napari/napari/\#5447) + +Bugfix: Ensure layer.\_fixed\_vertex is set when rotating (napari/napari/\#5449) + +Fix \_n\_selected\_points in \_layerlist\_context.py (napari/napari/\#5450) + +Refactor Main Window status bar to improve information presentation (napari/napari/\#5451) + +Bugfix: Fix test\_get\_system\_theme test for name to id change (napari/napari/\#5456) + +Bugfix: {POLL}\_INTERVAL\_MS used in {QTimer} needs to be an int on python 3.10 (napari/napari/\#5467) + +Bugfix: Add missing Enums and Flags required by {PySide}6 {\textgreater} 6.4 (napari/napari/\#5480) + +{BugFix}: napari does not start with Python v3.11.1: "{ValueError}: A distribution name is required." (napari/napari/\#5482) + +Fix inverted {LUT} and blending (napari/napari/\#5487) + +Fix opening file dialogs in {PySide} (napari/napari/\#5492) + +Handle case when {QtDims} play thread is partially deleted (napari/napari/\#5499) + +Ensure surface normals and wireframes are using Models internally (napari/napari/\#5501) + +Recursively check for dependent property to fire events. (napari/napari/\#5528) + +Set {PYTHONEXECUTABLE} as part of macos fixes on (re)startup (napari/napari/\#5531) + +Un-set unified title and tool bar on mac (Qt property) (napari/napari/\#5533) + +Fix key error issue of action manager (napari/napari/\#5539) + +Bugfix: ensure Checkbox state comparisons are correct by using Qt.{CheckState}(state) (napari/napari/\#5541) + +Clean dangling widget in test (napari/napari/\#5544) + +Fix test\_worker\_with\_progress by wait on worker end (napari/napari/\#5548) + +Fix min req (napari/napari/\#5560) + +Fix vispy axes labels (napari/napari/\#5565) + +Fix colormap utils error suggestion code and add a test (napari/napari/\#5571) + +Fix problem of missing plugin widgets after minimize napari (napari/napari/\#5577) + +Make point size isotropic (napari/napari/\#5582) + +Fix guard of qt import in napari.utils.theme (napari/napari/\#5593) + +Fix empty shapes layer duplication and Convert to Labels enablement logic for selected empty shapes layers (napari/napari/\#5594) + +Stop using removed multichannel= kwarg to skimage functions (napari/napari/\#5596) + +Add information about syntax\_style value in error message for theme validation (napari/napari/\#5602) + +Remove catch\_warnings in slicing (napari/napari/\#5603) + +Incorret theme should not prevent napari from start (napari/napari/\#5605) + +Unblock axis labels event to be emitted when slider label changes (napari/napari/\#5631) + +Bugfix: {IndexError} slicing Surface with higher-dimensional vertex\_values (napari/napari/\#5635) + +Bugfix: Convert Viewer Delete button to {QtViewerPushButton} with action and shortcut (napari/napari/\#5636) + +Change dim axis\_label resize logic to set width using only displayed labels width (napari/napari/\#5640) + +Feature: support for textures and vertex colors on Surface layers (napari/napari/\#5642) + +Fix features issues with init param and property setter (napari/napari/\#5646) + +Bugfix: Don't double toggle visibility for linked layers (napari/napari/\#5656) + +Bugfix: ensure pan/zoom buttons work, along with spacebar keybinding (napari/napari/\#5669) + +Bugfix: Add Tracks to qt\_keyboard\_settings (napari/napari/\#5678) + +Fix automatic naming and {GUI} exposure of multiple unnamed colormaps (napari/napari/\#5682) + +Fix mouse movement handling for {TransformBoxOverlay} (napari/napari/\#5692) + +Update environment.yml (napari/napari/\#5693) + +Resolve symlinks from path to environment for setting path (napari/napari/\#5704) + +Fix tracks color-by when properties change (napari/napari/\#5708) + +Fix Sphinx warnings (napari/napari/\#5717) + +Do not use depth for canvas overlays; allow setting blending mode for overlays (napari/napari/\#5720) + +Unify event behaviour for points and its qt controls (napari/napari/\#5722) + +Fix camera 3D absolute rotation bug (napari/napari/\#5726) + +Maint: Bump mypy (napari/napari/\#5727) + +Style {QGroupBox} indicator (napari/napari/\#5729) + +Fix centering of non-displayed dimensions (napari/napari/\#5736) + +Don't attempt to use npe1 readers in napari.plugins.\_npe2.read (napari/napari/\#5739) + +Prevent canvas micro-panning on point add (napari/napari/\#5742) + +Use text opacity to signal that widget is disabled (napari/napari/\#5745) + +Bugfix: Add the missed {keyReleaseEvent} method in {QtViewerDockWidget} (napari/napari/\#5746) + +Update status bar on active layer change (napari/napari/\#5754) + +Use array size directly when checking multiscale arrays to prevent overflow (napari/napari/\#5759) + +Fix path to check\_updated\_packages.py (napari/napari/\#5762) + +Brush cursor implementation using an overlay (napari/napari/\#5763) + +Bugfix: force a redraw to ensure highlight shows when Points are select-all selected (napari/napari/\#5771) + +Fix copy/paste of points (napari/napari/\#5795) + +Fix multiple viewer example (napari/napari/\#5796) + +Fix colormapping {nD} images (napari/napari/\#5805) + +Set focus policy for mainwindow to prevent keeping focus on the axis labels (and other {QLineEdit} based widgets) when clicking outside the widget (napari/napari/\#5812) + +Enforce Points.selected\_data type as Selection (napari/napari/\#5813) + +Change toggle menubar visibility functionality to hide menubar and show it on mouse movement validation (napari/napari/\#5824) + +Bugfix: Disconnect callbacks on object deletion in special functions from event\_utils (napari/napari/\#5826) + +Do not blend color in {QtColorBox} with black using opacity (napari/napari/\#5827) + +Don't allow negative contour values (napari/napari/\#5830) + +Bugfixes for layer overlays: clean up when layer is removed + fix potential double creation (napari/napari/\#5831) + +Add compatibility to {PySide} in file dialogs by using positional arguments (napari/napari/\#5834) + +Bugfix: fix broken "show selected" in the Labels layer (because of caching) (napari/napari/\#5841) + +Add tests for popup widgets and fix perspective popup slider initialization (napari/napari/\#5848) + +[Qt6] Fix {AttributeError} on renaming layer (napari/napari/\#5850) + +Bugfix: Ensure {QTableWidgetItem}(action.description) item is enabled (napari/napari/\#5854) + +Add constraints file during installation of packages from pip in docs workflow (napari/napari/\#5862) + +Bugfix: link the Labels model to the "show selected" checkbox (napari/napari/\#5867) + +Add \_\_all\_\_ to napari/types.py (napari/napari/\#5894) + +Fix drawing vertical or horizontal line segments in Shapes layer (napari/napari/\#5895) + +Disallow outside screen geometry napari window position (napari/napari/\#5915) + +Fix napari-svg version parsing in conftest.py (napari/napari/\#5947) + +Fix issue in utils.progress for disable=True (napari/napari/\#5964) + +Set high {DPI} attributes when using {PySide}2 (napari/napari/\#5968) + +[0.4.18rc1] Bugfix/event proxy (napari/napari/\#5994) + +Fix behavior of {PublicOnlyProxy} in setattr, wrapped methods, and calling (napari/napari/\#5997) + +Bugfix: Fix regression from \#5739 for passing plugin name and reader plus add test (napari/napari/\#6013) + +Avoid passing empty string to importlib.metadata.metadata (napari/napari/\#6018) + +Use tuple for pip constraints to avoid {LRU} cache error (napari/napari\#6036 + +{API} Changes + + + +Overlays 2.0 (napari/napari/\#4894) + +expose custom image interpolation kernels (napari/napari/\#5130) + +Connect image to plane events and expose them (napari/napari/\#5131) + +Deprecations +Build Tools + + + +ci(dependabot): bump styfle/cancel-workflow-action from 0.10.0 to 0.10.1 (napari/napari/\#5158) + +ci(dependabot): bump actions/checkout from 2 to 3 (napari/napari/\#5160) + +ci(dependabot): bump styfle/cancel-workflow-action from 0.10.1 to 0.11.0 (napari/napari/\#5290) + +ci(dependabot): bump docker/login-action from 2.0.0 to 2.1.0 (napari/napari/\#5291) + +ci(dependabot): bump actions/upload-artifact from 2 to 3 (napari/napari/\#5292) + +Pin mypy version (napari/napari/\#5310) + +{MAINT}: Start testing on Python 3.11 in {CI}. (napari/napari/\#5439) + +Pin test dependencies (napari/napari/\#5715) + +Documentation + + + +Fix failure on benchmark reporting (napari/napari/\#5083) + +Add {NAP}-5: proposal for an updated napari logo (napari/napari/\#5084) + +{DOC} Update doc contributing guide (napari/napari/\#5114) + +Napari debugging during plugin development documentation (napari/napari/\#5142) + +{DOC} Update docstring of add\_dock\_widget \& \_add\_viewer\_dock\_widget (napari/napari/\#5173) + +Specified that the path is to the local folder in contributing documentation guide. (napari/napari/\#5191) + +Fixes broken links in latest docs version (napari/napari/\#5193) + +Fixes gallery {ToC} (napari/napari/\#5458) + +Fix broken link in {EmitterGroup} docstring (napari/napari/\#5465) + +Fix Sphinx warnings (napari/napari/\#5717) + +Add Fourier transform playground example (napari/napari/\#5872) + +Improve documentation of changed event in {EventedList} (napari/napari/\#5928) + +Set removal version in deprecation of Viewer.rounded\_division (napari/napari/\#5944) + +Update docs using changes from napari/docs (napari/napari/\#5979) + +Update {CITATION}.cff file with 0.4.18 contributors (napari/napari/\#5980) + +Pre commit fixes for 0.4.18 release branch (napari/napari/\#5985) + +Port changes from docs repo to main repo for 0.4.18 (napari/napari/\#6002) + +Add favicon and configuration (napari/docs/\#4) + +Docs for 5195 from main repository (napari/docs/\#7) + +Use imshow in getting\_started (napari/docs/\#9) + +{DOC} Update viewer.md (napari/docs/\#11) + +Add and/or update documentation alt text (napari/docs/\#12) + +Adding documents and images from January 2022 plugin testing workshop. (napari/docs/\#35) + +Add some more docs about packaging details and conda-forge releases (napari/docs/\#48) + +Add documentation on using virtual environments for testing in napari based on 2022-01 workshop by Talley Lambert (napari/docs/\#50) + +Added info for conda installation problems (napari/docs/\#51) + +add best practices about packaging (napari/docs/\#52) + +Update viewer tutorial, regarding the console button (napari/docs/\#53) + +add sample database page (napari/docs/\#56) + +Fix magicgui objects.inv url for intersphinx (napari/docs/\#58) + +Fix broken links (napari/docs/\#59) + +Add sphinx-design cards to Usage landing page (napari/docs/\#63) + +Update to napari viewer tutorial. (napari/docs/\#65) + +Added environment creation and doc tools install (napari/docs/\#72) + +Feature: add copy button for code blocks using sphinx-copybutton (napari/docs/\#76) + +Add {NAP}-6 - Proposal for contributable menus (napari/docs/\#77) + +Update contributing docs for [dev] install change needing Qt backend install (napari/docs/\#78) + +Update theme related documentation (napari/docs/\#81) + +Feature: implement python version substitution in conf.py (napari/docs/\#84) + +Fixes gallery {ToC} (napari/docs/\#85) + +Clarify arm64 {macOS} (Apple Silicon) installation (napari/docs/\#89) + +Add cards to usage landing pages (napari/docs/\#97) + +Replace pip with python -m pip (napari/docs/\#100) + +change blob example to be self contained (napari/docs/\#101) + +Home page update, take 2 (napari/docs/\#102) + +Update the 'ensuring correctness' mission clause (napari/docs/\#105) + +Update steering council listing on website (napari/docs/\#106) + +Update version switcher json (napari/docs/\#109) + +Installation: Add libmamba solver to conda Note (napari/docs/\#110) + +Update requirements and config for sphinx-favicon for 1.0 (napari/docs/\#116) + +change print to f-string (napari/docs/\#117) + +Replace non-breaking spaces with regular spaces (napari/docs/\#118) + +Bugfix: documentation update for napari {PR} \#5636 (napari/docs/\#123) + +Add matplotlib image scraper for gallery (napari/docs/\#130) + +Fix missing links and references (napari/docs/\#133) + +Update {URL} of version switcher (napari/docs/\#139) + +Harmonize release notes to new mandatory labels (napari/docs/\#141) + +Update installation docs to remove briefcase bundle mentions (napari/docs/\#147) + +Fix version switcher {URL} for the latest docs version (napari/docs/\#148) + +Update Shapes How-To for new Lasso tool (napari/\#5555) (napari/docs/\#149) + +Fix signpost to make\_napari\_viewer code (napari/docs/\#151) + +Docs for adding {LayerData} tuple to viewer (napari/docs/\#152) + +Update viewer tutorial 3D mode docs (napari/docs/\#159) + +Add a napari plugin debugging quick start section to the debugging guide (napari/docs/\#161) + +Pin npe2 version to match installed one (napari/docs/\#175) + +Add Wouter-Michiel Vierdag to list of core devs (napari/docs/\#181) + +Update {SC} information (napari/docs/\#192) + +Other Pull Requests + + + +use app-model for view menu (napari/napari/\#4826) + +Overlay backend refactor (napari/napari/\#4907) + +Migrate help menu to use app model (napari/napari/\#4922) + +Refactor layer slice/dims/view/render state (napari/napari/\#5003) + +{MAINT}: increase min numpy version. (napari/napari/\#5089) + +Refactor qt notification and its test solve problem of segfaults (napari/napari/\#5138) + +Decouple changing viewer.theme from changing theme settings/preferences (napari/napari/\#5143) + +[{DOCS}] misc invalid syntax updates. (napari/napari/\#5176) + +{MAINT}: remove vendored colorconv from skimage. (napari/napari/\#5180) + +Re-add {README} screenshot (napari/napari/\#5220) + +{MAINT}: remove requirements.txt and cache actions based on setup.cfg. (napari/napari/\#5234) + +Explicitly set test array data to fix a flaky test (napari/napari/\#5245) + +Add ruff linter to pre-commit (napari/napari/\#5275) + +Run tests on release branch (napari/napari/\#5277) + +Vispy 0.12: per-point symbol and several bugfixes (napari/napari/\#5312) + +Make all imports absolute (napari/napari/\#5318) + +Fix track ids features ordering for unordered tracks (napari/napari/\#5320) + +tests: remove private magicgui access in tests (napari/napari/\#5331) + +Make settings and cache separate per each environment. (napari/napari/\#5333) + +Remove internal event connection on {SelectableEventedList} (napari/napari/\#5339) + +Unset {PYTHON}* vars and use entitlements in {macOS} conda menu shortcut (napari/napari/\#5354) + +Distinguish between update\_dims, extent changes, and refresh (napari/napari/\#5363) + +Add checks for pending Qt threads and timers in tests (napari/napari/\#5373) + +Suppress color conversion warning when converting invalid {LAB} coordinates (napari/napari/\#5386) + +Fix warning when fail to import qt binding. (napari/napari/\#5388) + +Update {MANIFEST}.in to remove warning when run tox (napari/napari/\#5393) + +[Automatic] Update albertosottile/darkdetect vendored module (napari/napari/\#5394) + +Update citation metadata (napari/napari/\#5398) + +Feature: making the Help menu more helpful via weblinks (re-do of \#5094) (napari/napari/\#5399) + +[pre-commit.ci] pre-commit autoupdate (napari/napari/\#5403) + +Fix flaky dims playback test by waiting for playing condition (napari/napari/\#5414) + +[Automatic] Update albertosottile/darkdetect vendored module (napari/napari/\#5416) + +[pre-commit.ci] pre-commit autoupdate (napari/napari/\#5422) + +Avoid setting corner pixels for empty layers (napari/napari/\#5423) + +Maint: Typing and {ImportError} -{\textgreater} {ModuleNotFoundError}. (napari/napari/\#5431) + +Fix tox passenv setup for {DISPLAY} and {XAUTHORITY} environment variables (napari/napari/\#5441) + +Add error color to themes and change application close/exit dialogs (napari/napari/\#5442) + +Update screenshot in readme (napari/napari/\#5452) + +Maint: Fix sporadic {QtDims} garbage collection failures by converting some stray references to weakrefs (napari/napari/\#5471) + +Replace {GabrielBB}/xvfb-action (napari/napari/\#5478) + +Add tags to recently added examples (napari/napari/\#5486) + +Remove layer ndisplay event (napari/napari/\#5491) + +{MAINT}: Don't format logs in log call (napari/napari/\#5504) + +Replace flake8, isort and pyupgrade by ruff, enable additional usefull rules (napari/napari/\#5513) + +Second {PR} that enables more ruff rules. (napari/napari/\#5520) + +Use pytest-pretty for better log readability (napari/napari/\#5525) + +{MAINT}: Follow Nep29, bump minimum numpy. (napari/napari/\#5532) + +[pre-commit.ci] pre-commit autoupdate (napari/napari/\#5534) + +Move layer editable change from slicing to controls (napari/napari/\#5546) + +update conda\_menu\_config.json for latest fixes in menuinst (napari/napari/\#5564) + +Enable the {COM} and {SIM} rules in ruff configuration (napari/napari/\#5566) + +Move from ubuntu 18.04 to ubuntu 20.04 in workflows (napari/napari/\#5578) + +{FIX}: Fix --pre skimage that have a more precise warning message (napari/napari/\#5580) + +Remove leftover duplicated code (napari/napari/\#5586) + +Remove napari-hub {API} access code (napari/napari/\#5587) + +Enable ruff rules part 4. (napari/napari/\#5590) + +[pre-commit.ci] pre-commit autoupdate (napari/napari/\#5592) + +Maint: {ImportError} -{\textgreater} {ModuleNotFoundError}. (napari/napari/\#5628) + +[pre-commit.ci] pre-commit autoupdate (napari/napari/\#5645) + +{MAINT}: Do not use mutable default for dataclass. (napari/napari/\#5647) + +{MAINT}: Do not use cgi-traceback on 3.11+ (deprecated, marked for removal) (napari/napari/\#5648) + +{MAINT}: Add explicit level to warn. (napari/napari/\#5649) + +{MAINT}: Split test file in two to find hanging test. (napari/napari/\#5680) + +Skip pyside6 version 6.4.3 for tests (napari/napari/\#5683) + +Pin pydantic. (napari/napari/\#5695) + +fix test\_viewer\_open\_no\_plugin exception message expectation (napari/napari/\#5698) + +fix: Block {PySide}6==6.5.0 in tests (napari/napari/\#5702) + +Don't resize shape after Shift release until mouse moves (napari/napari/\#5707) + +Update test\_examples job dependencies, unskip surface\_timeseries\_.py and update some examples validations (napari/napari/\#5716) + +Add test to check basic interactions with layer controls widgets (napari/napari/\#5757) + +test: [Automatic] Constraints upgrades: dask, hypothesis, imageio, npe2, numpy, pandas, psutil, pygments, pytest, rich, tensorstore, tifffile, virtualenv, xarray (napari/napari/\#5776) + +[{MAINT}, packaging] Remove support for briefcase installers (napari/napari/\#5804) + +Update {PIP}\_CONSTRAINT value to fix failing comprehensive jobs (napari/napari/\#5809) + +[pre-commit.ci] pre-commit autoupdate (napari/napari/\#5836) + +[pre-commit.ci] pre-commit autoupdate (napari/napari/\#5860) + +Fix Dev Docker Container (napari/napari/\#5877) + +Make mypy error checking opt-out instead of opt-in (napari/napari/\#5885) + +Update Error description when plugin not installed (napari/napari/\#5899) + +maint: add fixture to disable throttling (napari/napari/\#5908) + +Update upgrade dependecies and test workflows (napari/napari/\#5919) + +[Maint] Fix comprehensive tests by skipping labels controls test on py311 pyqt6 (napari/napari/\#5922) + +Fix typo in resources/requirements\_mypy.in file name (napari/napari/\#5924) + +Add Python 3.11 trove classifier. (napari/napari/\#5937) + +Change license\_file to license\_files in setup.cfg (napari/napari/\#5948) + +test: [Automatic] Constraints upgrades: dask, fsspec, hypothesis, imageio, ipython, napari-plugin-manager, napari-svg, numpy, psygnal, pydantic, pyqt6, pytest, rich, scikit-image, virtualenv, zarr (napari/napari/\#5963) + +Update deprecation information (napari/napari/\#5984) + +Pin napari and pydantic when installing a plugin (napari/napari/\#6022) + +40 authors added to this release (alphabetical) + + + +Alister Burt - @alisterburt + +Andrea Pierré - @kir0ul + +Andrew Sweet - @andy-sweet + +Ashley Anderson - @aganders3 + +Clément Caporal - @{ClementCaporal} + +Constantin Pape - @constantinpape + +Craig T. Russell - @ctr26 + +Daniel Althviz Moré - @dalthviz + +David Ross - @davidpross + +David Stansby - @dstansby + +Gabriel Selzer - @gselzer + +Gonzalo Peña-Castellanos - @goanpeca + +Gregor Lichtner - @glichtner + +Grzegorz Bokota - @Czaki + +Jaime Rodríguez-Guerra - @jaimergp + +Jan-Hendrik Müller - @kolibril13 + +Jannis Ahlers - @jnahlers + +Jessy Lauer - @jeylau + +Jonas Windhager - @jwindhager + +Jordão Bragantini - @{JoOkuma} + +Juan Nunez-Iglesias - @jni + +Jules Vanaret - @jules-vanaret + +Kabilar Gunalan - @kabilar + +Katherine Hutchings - @katherine-hutchings + +Kim Pevey - @kcpevey + +Konstantin Sofiiuk - @ksofiyuk + +Kyle I. S. Harrington - @kephale + +Lorenzo Gaifas - @brisvag + +Luca Marconato - @{LucaMarconato} + +Lucy Liu - @lucyleeow + +Mark Harfouche - @hmaarrfk + +Matthias Bussonnier - @Carreau + +Melissa Weber Mendonça - @melissawm + +Nadalyn Miller - @Nadalyn-{CZI} + +Pam Wadhwa - @ppwadhwa + +Paul Smith - @p-j-smith + +Peter Sobolewski - @{psobolewskiPhD} + +Sean Martin - @seankmartin + +Talley Lambert - @tlambert03 + +Wouter-Michiel Vierdag - @melonora + +43 reviewers added to this release (alphabetical) + + + +Alan Lowe - @quantumjot + +Alister Burt - @alisterburt + +Andrew Sweet - @andy-sweet + +Ashley Anderson - @aganders3 + +Charlie Marsh - @charliermarsh + +Daniel Althviz Moré - @dalthviz + +David Ross - @davidpross + +David Stansby - @dstansby + +Draga Doncila Pop - @{DragaDoncila} + +Eric Perlman - @perlman + +Gabriel Selzer - @gselzer + +Genevieve Buckley - @{GenevieveBuckley} + +Gonzalo Peña-Castellanos - @goanpeca + +Grzegorz Bokota - @Czaki + +Isabela Presedo-Floyd - @isabela-pf + +Jaime Rodríguez-Guerra - @jaimergp + +Jan-Hendrik Müller - @kolibril13 + +Jessy Lauer - @jeylau + +Jordão Bragantini - @{JoOkuma} + +Juan Nunez-Iglesias - @jni + +Jules Vanaret - @jules-vanaret + +Kevin Yamauchi - @kevinyamauchi + +Kim Pevey - @kcpevey + +Kira Evans - @kne42 + +Konstantin Sofiiuk - @ksofiyuk + +Kyle I. S. Harrington - @kephale + +Lorenzo Gaifas - @brisvag + +Luca Marconato - @{LucaMarconato} + +Lucy Liu - @lucyleeow + +Lucy Obus - @{LCObus} + +Mark Harfouche - @hmaarrfk + +Matthias Bussonnier - @Carreau + +Melissa Weber Mendonça - @melissawm + +Nathan Clack - @nclack + +Nicholas Sofroniew - @sofroniewn + +Oren Amsalem - @orena1 + +Pam Wadhwa - @ppwadhwa + +Paul Smith - @p-j-smith + +Peter Sobolewski - @{psobolewskiPhD} + +Sean Martin - @seankmartin + +Talley Lambert - @tlambert03 + +Wouter-Michiel Vierdag - @melonora + +Ziyang Liu - @liu-ziyang + +19 docs authors added to this release (alphabetical) + + + +Ashley Anderson - @aganders3 + +chili-chiu - @chili-chiu + +Christopher Nauroth-Kreß - @Chris-N-K + +Curtis Rueden - @ctrueden + +Daniel Althviz Moré - @dalthviz + +David Stansby - @dstansby + +Draga Doncila Pop - @{DragaDoncila} + +Grzegorz Bokota - @Czaki + +Jaime Rodríguez-Guerra - @jaimergp + +Juan Nunez-Iglesias - @jni + +Lorenzo Gaifas - @brisvag + +Lucy Liu - @lucyleeow + +Matthias Bussonnier - @Carreau + +Melissa Weber Mendonça - @melissawm + +Nadalyn Miller - @Nadalyn-{CZI} + +Oren Amsalem - @orena1 + +Peter Sobolewski - @{psobolewskiPhD} + +Sean Martin - @seankmartin + +Wouter-Michiel Vierdag - @melonora + +20 docs reviewers added to this release (alphabetical) + + + +Alister Burt - @alisterburt + +Andrew Sweet - @andy-sweet + +Ashley Anderson - @aganders3 + +Christopher Nauroth-Kreß - @Chris-N-K + +David Stansby - @dstansby + +Draga Doncila Pop - @{DragaDoncila} + +Gonzalo Peña-Castellanos - @goanpeca + +Grzegorz Bokota - @Czaki + +Jaime Rodríguez-Guerra - @jaimergp + +Juan Nunez-Iglesias - @jni + +Kevin Yamauchi - @kevinyamauchi + +Kira Evans - @kne42 + +Lorenzo Gaifas - @brisvag + +Lucy Liu - @lucyleeow + +Melissa Weber Mendonça - @melissawm + +Nadalyn Miller - @Nadalyn-{CZI} + +Nicholas Sofroniew - @sofroniewn + +Peter Sobolewski - @{psobolewskiPhD} + +Sean Martin - @seankmartin + +Wouter-Michiel Vierdag - @melonora + +New Contributors + +There are 19 new contributors for this release: + + + +Christopher Nauroth-Kreß docs - @Chris-N-K + +Clément Caporal napari - @{ClementCaporal} + +Constantin Pape napari - @constantinpape + +Craig T. Russell napari - @ctr26 + +Daniel Althviz Moré docs napari - @dalthviz + +David Ross napari - @davidpross + +Gregor Lichtner napari - @glichtner + +Jannis Ahlers napari - @jnahlers + +Jessy Lauer napari - @jeylau + +Jules Vanaret napari - @jules-vanaret + +Kabilar Gunalan napari - @kabilar + +Katherine Hutchings napari - @katherine-hutchings + +Konstantin Sofiiuk napari - @ksofiyuk + +{LucaMarconato} napari - @{LucaMarconato} + +Nadalyn Miller docs napari - @Nadalyn-{CZI} + +Oren Amsalem docs - @orena1 + +Paul Smith napari - @p-j-smith + +Sean Martin docs napari - @seankmartin + +Wouter-Michiel Vierdag docs napari - @melonora}, + version = {v0.4.18}, + publisher = {Zenodo}, + author = {Ahlers, Jannis and Althviz Moré, Daniel and Amsalem, Oren and Anderson, Ashley and Bokota, Grzegorz and Boone, Peter and Bragantini, Jordão and Buckley, Genevieve and Burt, Alister and Bussonnier, Matthias and Can Solak, Ahmet and Caporal, Clément and Doncila Pop, Draga and Evans, Kira and Freeman, Jeremy and Gaifas, Lorenzo and Gohlke, Christoph and Gunalan, Kabilar and Har-Gil, Hagai and Harfouche, Mark and Harrington, Kyle I. S. and Hilsenstein, Volker and Hutchings, Katherine and Lambert, Talley and Lauer, Jessy and Lichtner, Gregor and Liu, Ziyang and Liu, Lucy and Lowe, Alan and Marconato, Luca and Martin, Sean and {McGovern}, Abigail and Migas, Lukasz and Miller, Nadalyn and Muñoz, Hector and Müller, Jan-Hendrik and Nauroth-Kreß, Christopher and Nunez-Iglesias, Juan and Pape, Constantin and Pevey, Kim and Peña-Castellanos, Gonzalo and Pierré, Andrea and Rodríguez-Guerra, Jaime and Ross, David and Royer, Loic and Russell, Craig T. and Selzer, Gabriel and Smith, Paul and Sobolewski, Peter and Sofiiuk, Konstantin and Sofroniew, Nicholas and Stansby, David and Sweet, Andrew and Vierdag, Wouter-Michiel and Wadhwa, Pam and Weber Mendonça, Melissa and Windhager, Jonas and Winston, Philip and Yamauchi, Kevin}, + urldate = {2024-04-28}, + date = {2023-07-05}, + doi = {10.5281/zenodo.8115575}, + file = {Snapshot:/Users/aj/Zotero/storage/VX4YHQCL/8115575.html:text/html}, } diff --git a/paper/paper.md b/paper/paper.md index 30bdac1a..23217cdb 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -31,7 +31,7 @@ Multiplexed imaging data are revolutionizing our understanding of the compositio A variety of methods have been introduced for high multiplexed imaging of tissues, including MxIF, CyCIF, CODEX, 4i, mIHC, MIBI, IBEX, and IMC [@angelo_multiplexed_2014; @gerdes_highly_2013; @giesen_highly_2014; @goltsev_deep_2018; @gut_multiplexed_2018; @tsujikawa_quantitative_2017; @lin_highly_2018]; although these methods differ in their implementations, all enable the collection of single-cell data on 20-100 proteins within preserved 2D and 3D tissue microenvironments. Analysis of high-plex images typically involves joining adjacent image tiles together and aligning channels across imaging cycles (stitching and registration) to generate a composite high-plex image and then identifying the positions and boundaries of individual cells via segmentation. The intensities of individual protein antigens, stains, and other detectable molecules are then quantified on a per-cell basis. This generates a “spatial feature table” (analogous to a count table in sequencing) that can be used to identify individual cell types and states; tools from spatial statistics are then used to identify how these cells are patterned in space from scales ranging from a few cell diameters (~10 µm) to several millimeters. -Spatial feature tables provide the quantitative data for analysis of high-plex data but human inspection of the original image data remains essential. At the current state of the art, many of the critical morphological details in high-resolution images cannot be fully and accurately quantified. Segmentation is also subject to errors identifiable by humans, but not fully resolvable computationally [@baker_quality_2024]. As a consequence, computation of spatial features and relationships must be performed in combination with visualization of the underlying image data. Humans excel at identifying tissue features that correspond to classical histo-morphologies; they are also effective at discriminating foreground signals from variable background [@nirmal_cell_2023] using a process of “visual gating” (perception of high and low-intensity levels while visualizing an image). More generally, effective integration of visualization and computation enables nuanced interpretation of cellular organization in relation to established tissue architectures. `SCIMAP` uses the Python-based Napari [@chiu_napari_2022] image viewer to leverage these capabilities by providing a seamless interface to inspect and annotate high-plex imaging data alongside computational analysis. For example, we have implemented an image-based gating approach that allows users to visually determine the threshold that discriminates background from a true signal at both a whole-specimen and single-cell level. Users can also select specific regions of interest (ROIs) for selective or deeper analysis. This involves drawing ROIs over images (freehand or geometric) and then selecting the underlying single data for further analysis. This capability is essential for incorporating histopathological information on common tissue structures (e.g., epidermis, dermis, follicles), immune structures (e.g., secondary and tertiary lymphoid structures), tumor domains (e.g., tumor center, boundary, tumor buds), and tumor grade or stage (e.g., early lesions, invasive regions, established nodules). It also allows for excluding regions affected by significant tissue loss, folding, or artifactual staining [@baker_quality_2024]. `SCIMAP` then performs statistical and spatial analyses on individual ROIs or sets of ROIs. Spatial analysis, including the measurement of distances between cells, analysis of interaction patterns, categorization into neighborhoods, and scoring of these patterns, is crucial for elucidating the cellular communications that underpin the functional aspects of the biology being studied. `SCIMAP` offers various functions to facilitate these analyses. +Spatial feature tables provide the quantitative data for analysis of high-plex data but human inspection of the original image data remains essential. At the current state of the art, many of the critical morphological details in high-resolution images cannot be fully and accurately quantified. Segmentation is also subject to errors identifiable by humans, but not fully resolvable computationally [@baker_quality_2024]. As a consequence, computation of spatial features and relationships must be performed in combination with visualization of the underlying image data. Humans excel at identifying tissue features that correspond to classical histo-morphologies; they are also effective at discriminating foreground signals from variable background [@nirmal_cell_2023] using a process of “visual gating” (perception of high and low-intensity levels while visualizing an image). More generally, effective integration of visualization and computation enables nuanced interpretation of cellular organization in relation to established tissue architectures. `SCIMAP` uses the Python-based Napari [@chiu_napari_2022; @ahlers_napari_2023] image viewer to leverage these capabilities by providing a seamless interface to inspect and annotate high-plex imaging data alongside computational analysis. For example, we have implemented an image-based gating approach that allows users to visually determine the threshold that discriminates background from a true signal at both a whole-specimen and single-cell level. Users can also select specific regions of interest (ROIs) for selective or deeper analysis. This involves drawing ROIs over images (freehand or geometric) and then selecting the underlying single cell data for further analysis. This capability is essential for incorporating histopathological information on common tissue structures (e.g., epidermis, dermis, follicles), immune structures (e.g., secondary and tertiary lymphoid structures), tumor domains (e.g., tumor center, boundary, tumor buds), and tumor grade or stage (e.g., early lesions, invasive regions, established nodules). It also allows for excluding regions affected by significant tissue loss, folding, or artifactual staining [@baker_quality_2024]. `SCIMAP` then performs statistical and spatial analyses on individual ROIs or sets of ROIs. Spatial analysis, including the measurement of distances between cells, analysis of interaction patterns, categorization into neighborhoods, and scoring of these patterns, is crucial for elucidating the cellular communications that underpin the functional aspects of the biology being studied. `SCIMAP` offers various functions to facilitate these analyses. Lastly, a single high-plex whole slide image can exceed 100GB per image and 10$^6$ cells, necessitating optimized functions for handling large matrices and images. `SCIMAP` employs the well-established AnnData object structure, complemented by Dask and Zarr for efficient image loading in Napari. This approach facilitates seamless viewing of images with overlaid data layers, thus enabling effective analysis of large datasets. To date, `SCIMAP` has been used in the analysis of over 5 datasets from 8 tissue and cancer types [@yapp_multiplexed_2024; @nirmal_spatial_2022; @gaglia_lymphocyte_2023; @maliga_immune_2024].