diff --git a/joss.06317/10.21105.joss.06317.crossref.xml b/joss.06317/10.21105.joss.06317.crossref.xml new file mode 100644 index 0000000000..429605c486 --- /dev/null +++ b/joss.06317/10.21105.joss.06317.crossref.xml @@ -0,0 +1,286 @@ + + + + 20240207T213731-503810327906f70a4bd187ed3df3dd546298e5d4 + 20240207213731 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 02 + 2024 + + + 9 + + 94 + + + + HistoJS: Web-Based Analytical Tool for Advancing +Multiplexed Images + + + + Mohamed + Masoud + https://orcid.org/0000-0002-5365-242X + + + David + Gutman + https://orcid.org/0000-0002-1386-8701 + + + Sergey + Plis + https://orcid.org/0000-0003-0040-0365 + + + + 02 + 07 + 2024 + + + 6317 + + + 10.21105/joss.06317 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10626533 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6317 + + + + 10.21105/joss.06317 + https://joss.theoj.org/papers/10.21105/joss.06317 + + + https://joss.theoj.org/papers/10.21105/joss.06317.pdf + + + + + + The digital slide archive: A software +platform for management, integration, and analysis of histology for +cancer research + Gutman + Cancer research + 21 + 77 + 10.1158/0008-5472.CAN-17-0629 + 2017 + Gutman, D. A., Khalilia, M., Lee, S., +Nalisnik, M., Mullen, Z., Beezley, J., Chittajallu, D. R., Manthey, D., +& Cooper, L. A. (2017). The digital slide archive: A software +platform for management, integration, and analysis of histology for +cancer research. Cancer Research, 77(21), e75–e78. +https://doi.org/10.1158/0008-5472.CAN-17-0629 + + + OpenSeadragon + OpenSeadragon dev. team + 2022 + OpenSeadragon dev. team. (2022). +OpenSeadragon. GitHub Pages. +http://openseadragon.github.io/ + + + Highly multiplexed imaging of single cells +using a high-throughput cyclic immunofluorescence method + Lin + Nature communications + 1 + 6 + 10.1038/ncomms9390 + 2015 + Lin, J.-R., Fallahi-Sichani, M., +& Sorger, P. K. (2015). Highly multiplexed imaging of single cells +using a high-throughput cyclic immunofluorescence method. Nature +Communications, 6(1), 8390. +https://doi.org/10.1038/ncomms9390 + + + Highly multiplexed immunofluorescence imaging +of human tissues and tumors using t-CyCIF and conventional optical +microscopes + Lin + elife + 7 + 10.7554/eLife.31657 + 2018 + Lin, J.-R., Izar, B., Wang, S., Yapp, +C., Mei, S., Shah, P. M., Santagata, S., & Sorger, P. K. (2018). +Highly multiplexed immunofluorescence imaging of human tissues and +tumors using t-CyCIF and conventional optical microscopes. Elife, 7. +https://doi.org/10.7554/eLife.31657 + + + Highly multiplexed single-cell analysis of +formalin-fixed, paraffin-embedded cancer tissue + Gerdes + Proceedings of the National Academy of +Sciences + 29 + 110 + 10.1073/pnas.1300136110 + 2013 + Gerdes, M. J., Sevinsky, C. J., Sood, +A., Adak, S., Bello, M. O., Bordwell, A., Can, A., Corwin, A., Dinn, S., +Filkins, R. J., & others. (2013). Highly multiplexed single-cell +analysis of formalin-fixed, paraffin-embedded cancer tissue. Proceedings +of the National Academy of Sciences, 110(29), 11982–11987. +https://doi.org/10.1073/pnas.1300136110 + + + Cell detection with star-convex +polygons + Schmidt + Medical image computing and computer assisted +intervention–MICCAI 2018: 21st international conference, granada, spain, +september 16-20, 2018, proceedings, part II 11 + 10.1007/978-3-030-00934-2_30 + 2018 + Schmidt, U., Weigert, M., Broaddus, +C., & Myers, G. (2018). Cell detection with star-convex polygons. +Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: +21st International Conference, Granada, Spain, September 16-20, 2018, +Proceedings, Part II 11, 265–273. +https://doi.org/10.1007/978-3-030-00934-2_30 + + + A new statistical approach to geographic +variation analysis + Gabriel + Systematic zoology + 3 + 18 + 10.2307/2412323 + 1969 + Gabriel, K. R., & Sokal, R. R. +(1969). A new statistical approach to geographic variation analysis. +Systematic Zoology, 18(3), 259–278. +https://doi.org/10.2307/2412323 + + + Deep profiling of mouse splenic architecture +with CODEX multiplexed imaging + Goltsev + Cell + 4 + 174 + 10.1016/j.cell.2018.07.010 + 2018 + Goltsev, Y., Samusik, N., +Kennedy-Darling, J., Bhate, S., Hale, M., Vazquez, G., Black, S., & +Nolan, G. P. (2018). Deep profiling of mouse splenic architecture with +CODEX multiplexed imaging. Cell, 174(4), 968–981. +https://doi.org/10.1016/j.cell.2018.07.010 + + + Multiplexed ion beam imaging of human breast +tumors + Angelo + Nature medicine + 4 + 20 + 10.1038/nm.3488 + 2014 + Angelo, M., Bendall, S. C., Finck, +R., Hale, M. B., Hitzman, C., Borowsky, A. D., Levenson, R. M., Lowe, J. +B., Liu, S. D., Zhao, S., & others. (2014). Multiplexed ion beam +imaging of human breast tumors. Nature Medicine, 20(4), 436–442. +https://doi.org/10.1038/nm.3488 + + + QuPath: Open source software for digital +pathology image analysis + Bankhead + Scientific reports + 1 + 7 + 10.1038/s41598-017-17204-5 + 2017 + Bankhead, P., Loughrey, M. B., +Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, +S., Gray, R. T., Murray, L. J., Coleman, H. G., & others. (2017). +QuPath: Open source software for digital pathology image analysis. +Scientific Reports, 7(1), 1–7. +https://doi.org/10.1038/s41598-017-17204-5 + + + TensorFlow.js: Machine learning for the web +and beyond + Smilkov + arXiv + 10.48550/arXiv.1901.05350 + 2019 + Smilkov, D., Thorat, N., Assogba, Y., +Nicholson, C., Kreeger, N., Yu, P., Cai, S., Nielsen, E., Soegel, D., +& others. (2019). TensorFlow.js: Machine learning for the web and +beyond. arXiv. +https://doi.org/10.48550/arXiv.1901.05350 + + + Lung dataset-1 + Rashid + 10.7303/syn17865732 + 2019 + Rashid, R., & others. (2019). +Lung dataset-1. Synapse repository. +https://doi.org/10.7303/syn17865732 + + + Beyond authorship: Attribution, contribution, +collaboration, and credit + Brand + Learned Publishing + 2 + 28 + 10.1087/20150211 + 2015 + Brand, A., Allen, L., Altman, M., +Hlava, M., & Scott, J. (2015). Beyond authorship: Attribution, +contribution, collaboration, and credit. Learned Publishing, 28(2), +151–155. https://doi.org/10.1087/20150211 + + + + + + diff --git a/joss.06317/10.21105.joss.06317.jats b/joss.06317/10.21105.joss.06317.jats new file mode 100644 index 0000000000..67fb4717f8 --- /dev/null +++ b/joss.06317/10.21105.joss.06317.jats @@ -0,0 +1,556 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6317 +10.21105/joss.06317 + +HistoJS: Web-Based Analytical Tool for Advancing +Multiplexed Images + + + +https://orcid.org/0000-0002-5365-242X + +Masoud +Mohamed + + +* + + +https://orcid.org/0000-0002-1386-8701 + +Gutman +David + + + + +https://orcid.org/0000-0003-0040-0365 + +Plis +Sergey + + + + + + +Tri-institutional Center for Translational Research in +Neuroimaging and Data Science (TReNDS), Georgia State University, +Georgia Institute of Technology, Emory University, Atlanta, United +States of America + + + + +Department of Computer Science, Georgia State University, +Atlanta, United States of America + + + + +Department of Pathology, Emory University School of +Medicine, Atlanta, United States of America + + + + +* E-mail: + + +25 +12 +2023 + +9 +94 +6317 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Visualization +Web Machine Learning +Multiplexed Images +Spatial Biology +Single Cell + + + + + + Summary +

Advances in multiplexed imaging technologies enable us to capture + spatial single-cell proteomics and transcriptomics data in + unprecedented detail and with high spatial resolution. This large + volume of image data presents a challenge in accurately isolating and + quantifying distinct cell types to understand disease complexity, + neurological disorders, potential biomarkers, and targets for drug + development. Therefore, there is an incremental demand and challenges + to developing and validating cutting-edge quantitative image analysis + tools for diagnosis, prognosis, and therapy response prediction and + assessment in neurological and oncological diseases. + HistoJS + is a newly developed web-based tool that aims to overcome the + challenges of utilizing highly-multiplexed immunofluorescence (mIF) + images + (Lin + et al., 2015) for spatial biology research. It provides + open-source and extensible tool for analyzing spatial-molecular + patterns, enabling a deeper view of the single-cell spatial + relationship, along with machine learning algorithms in an easy-to-use + interactive interface for the biomedical community.

+ + + Statement of need +

Single-cell data have the potential to enhance our understanding of + biological systems, shedding light on disease mechanisms and + reactions. The data from single-cell sequencing and multiplexed + imaging technologies have distinctive capabilities for cell + phenotyping, clustering, and landscape analysis. However, handling + large-scale multiplexed image datasets generated from advanced imaging + technologies and narrowing the lag between those technologies and the + existing analytical tools remains challenging. There is a need to + effectively utilize subcellular imaging data by storing, retrieving, + visualizing, and performing quantitative analysis of those big data. + Addressing the challenges posed by highly multiplexed image analysis, + HistoJS emerges as an open-source and adaptable tool for visualizing + and analyzing intricate biological processes with spatial subcellular + resolution. It offers a graphical user interface for effortless + navigation through stored multiplexed images, allowing dynamic + selection of image channels for composite views. HistoJS encompasses a + diverse set of image processing and machine learning algorithms to + support essential analysis, including real-time cell segmentation, + phenotyping, classification, correlations, spatial analysis, and + quantification of cell types to unveil interactions within the tissue + samples. These functionalities are provided in an easy-to-use + interactive interface to help biomedical users and related groups + understand the progression of neurological and oncological diseases + and find clinical outcomes. Other commercial tools like the VisioPharm + suite are expensive and complicated to be customized by the + informatics community to meet the specific needs of cancer + researchers. QuPath + (Bankhead + et al., 2017), another popular open-source tool, has some + useful features for the analysis that are not web-based and need more + key features for whole slide image analysis. Positioned as a web-based + solution, HistoJS focuses on enhancing usability, accessibility, + sustainability, scalability, and collaboration. Boosting user-friendly + interfaces and cross-platform compatibility, HistoJS ensures a + seamless and accessible experience. The sustainability of HistoJS is + realized through centralized updates and reduced hardware + dependencies, streamlining management and minimizing hardware + requirements.

+ + + Background +

Advanced High-plex imaging technologies, such as Multiplexed + Immunofluorescence (MxIF) + (Gerdes + et al., 2013), tissue-based cyclic immunofluorescence (t-CyCIF) + (Lin + et al., 2018), CO-Detection by indEXing (CODEX) + (Goltsev + et al., 2018), and Multiplexed Ion Beam Imaging (MIBI) + (Angelo + et al., 2014), facilitate the study of intricate biological + processes with precise spatial subcellular resolution. While MIBI + utilizes a mass spectrometry-based approach, the other technologies + utilize conventional fluorescence microscopes, and all enable the + simultaneous detection of 50+ antigens within a single tissue section. + These technologies offer invaluable insights into the complexities of + biological systems. The process typically commences with sample + preparation of high-quality formaldehyde-fixed and paraffin-embedded + (FFPE) tissue sections (e.g., biopsy, surgical specimen) that can pass + through either an iterative, multicycle image acquisition pipeline as + CODEX or a non-iterative single imaging cycle, such as MIBI. Either + approach produces a stack of raw microscopy image tiles that need + stitching with drift correction and then registration across channels + to generate large-scale mosaic images in OME-TIFF (open microscopy + environment-tagged image file format). Each channel represents the + spectral signal from a specific marker or antigen in the tissue. Other + preprocessing steps include image deconvolution to minimize image + blurring, noise reduction to reduce background and autofluorescence + noise, and artifact removal that addresses image artifacts (e.g., + axial and lateral tile drift). Preprocessing steps are essential to + enhance the quality of the raw data and prepare it for downstream + analysis. The goal is to identify, localize, and count different cell + types to study their population and interactions.

+ + + Pipeline +

For hosting and organizing user data, the Digital Slide Archive + (DSA), an open-source platform, is used as a data hosting environment + (Gutman + et al., 2017). DSA is a reliable containerized web-based + platform that can store and manage large image datasets such as + immunofluorescence image data. DSA components include a MongoDB + database, a job execution/scheduler (girder worker), and a Secure data + management system (Girder) that provides RESTful APIs to allow + programmatic control over image data and metadata while providing user + access controls. DSA is easy to install locally on the user side or + remotely on the cloud. HistoJS uses DSA as a backend to manage user + images locally or remotely and store analysis results according to + end-user preferences. To visualize mIF images in HistoJS, + OpenSeadragon + (OpenSeadragon + dev. team, 2022), an open-source web-based viewer, is used. + HistoJS has rich JavaScript functions to support different types of + canvas rendering global Composite operations, giving the best insight + into data with panning, zooming, and overlay options. To that end, we + were able to make HistoJS suitable for building a customized stack of + protein channels and composite them in a high resolution and full + scale as illustrated in + [fig:HistoJS-Overview]. + The analytical components of HistoJS include cell segmentation, + phenotyping, classification, correlations, spatial analysis, and + quantification of cell types to discover cell interactions. We tackled + the challenges in real time; fast and reliable techniques are used to + expedite the extraction of cell boundaries + (Schmidt + et al., 2018) and morphological features (e.g., solidity, + eccentricity, orientation, etc.). Cell neighbor detections are tackled + with spatial plotting and analysis by computing Delaunay neighborhood + graphs + (Gabriel + & Sokal, 1969).

+ +

HistoJS graphical interface overview. Biological + statistical tasks such as biological cell biomarkers histogram, + sample statistics quartiles, cell classification, correlations, + spatial analysis, and quantification of specific marker expression + are available for cell analysis and discovering the cell + interactions. (Dataset + (Rashid + & others, + 2019)).

+ + +

Although the tool is suitable for cluster-based deployment, it can + also be deployed on the cloud or locally on the client side. With an + average GPU GeForce GTX 1050 Ti of 768 cores/ 4GB buffer, 7Gbps memory + speed, Intel® Core™ i7-8700 CPU @ 3.20GHz × 12, and a system memory of + 16 GB, HistoJS shows in general fast response while rendering and + processing immunofluorescence images, with the potential for better + performance thanks to the + TensorFlow.js(Smilkov + et al., 2019) Web Graphics Library ( WebGL-2) backbone.

+ + + Code availability +

HistoJS source code is publicly accessible in the GitHub repository + (https://github.com/Mmasoud1/HistoJS). + Multiple DSA online servers can be accessed from HistoJS such as + https://styx.neurology.emory.edu/girder/ + to load mIF data samples and test the tool performance. Researchers + could visualize and analyze the expression patterns of key biomarkers + associated with diseases and disorders. The platform’s interactive + features facilitated the identification of disease-specific + signatures, providing valuable insights into the molecular basis of + diseases.

+

Detailed step-by-step + documentation + is provided along with HistoJS + live + demo.

+ + + Author contributions +

We describe contributions to this paper using the CRediT taxonomy + (Brand + et al., 2015). Writing – Original Draft: M.M.; Writing – Review + & Editing: M.M., and S.P.; Conceptualization and methodology: + M.M., and D.G.; Software and data curation: M.M.; Validation: M.M., + and S.P.; Resources: D.G.; Visualization: M.M.; Project + Administration: M.M.

+ + + + + + + + GutmanDavid A + KhaliliaMohammed + LeeSanghoon + NalisnikMichael + MullenZach + BeezleyJonathan + ChittajalluDeepak R + MantheyDavid + CooperLee AD + + The digital slide archive: A software platform for management, integration, and analysis of histology for cancer research + Cancer research + AACR + 2017 + 77 + 21 + 10.1158/0008-5472.CAN-17-0629 + e75 + e78 + + + + + + OpenSeadragon dev. team + + OpenSeadragon + GitHub Pages + 2022 + http://openseadragon.github.io/ + + + + + + LinJia-Ren + Fallahi-SichaniMohammad + SorgerPeter K + + Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method + Nature communications + Nature Publishing Group UK London + 2015 + 6 + 1 + 10.1038/ncomms9390 + 8390 + + + + + + + LinJia-Ren + IzarBenjamin + WangShu + YappClarence + MeiShaolin + ShahParin M + SantagataSandro + SorgerPeter K + + Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes + elife + eLife Sciences Publications, Ltd + 2018 + 7 + 10.7554/eLife.31657 + + + + + + GerdesMichael J + SevinskyChristopher J + SoodAnup + AdakSudeshna + BelloMusodiq O + BordwellAlexander + CanAli + CorwinAlex + DinnSean + FilkinsRobert J + others + + Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue + Proceedings of the National Academy of Sciences + National Acad Sciences + 2013 + 110 + 29 + 10.1073/pnas.1300136110 + 11982 + 11987 + + + + + + SchmidtUwe + WeigertMartin + BroaddusColeman + MyersGene + + Cell detection with star-convex polygons + Medical image computing and computer assisted intervention–MICCAI 2018: 21st international conference, granada, spain, september 16-20, 2018, proceedings, part II 11 + Springer + 2018 + 10.1007/978-3-030-00934-2_30 + 265 + 273 + + + + + + GabrielK Ruben + SokalRobert R + + A new statistical approach to geographic variation analysis + Systematic zoology + Society of Systematic Zoology + 1969 + 18 + 3 + 10.2307/2412323 + 259 + 278 + + + + + + GoltsevYury + SamusikNikolay + Kennedy-DarlingJulia + BhateSalil + HaleMatthew + VazquezGustavo + BlackSarah + NolanGarry P + + Deep profiling of mouse splenic architecture with CODEX multiplexed imaging + Cell + Elsevier + 2018 + 174 + 4 + 10.1016/j.cell.2018.07.010 + 968 + 981 + + + + + + AngeloMichael + BendallSean C + FinckRachel + HaleMatthew B + HitzmanChuck + BorowskyAlexander D + LevensonRichard M + LoweJohn B + LiuScot D + ZhaoShuchun + others + + Multiplexed ion beam imaging of human breast tumors + Nature medicine + Nature Publishing Group US New York + 2014 + 20 + 4 + 10.1038/nm.3488 + 436 + 442 + + + + + + BankheadPeter + LoughreyMaurice B + FernándezJosé A + DombrowskiYvonne + McArtDarragh G + DunnePhilip D + McQuaidStephen + GrayRonan T + MurrayLiam J + ColemanHelen G + others + + QuPath: Open source software for digital pathology image analysis + Scientific reports + Nature Publishing Group + 2017 + 7 + 1 + 10.1038/s41598-017-17204-5 + 1 + 7 + + + + + + SmilkovD. + ThoratN. + AssogbaY. + NicholsonC. + KreegerN. + YuP. + CaiS. + NielsenE. + SoegelD. + others + + TensorFlow.js: Machine learning for the web and beyond + arXiv + 2019 + 10.48550/arXiv.1901.05350 + + + + + + RashidR. + others + + Lung dataset-1 + Synapse repository + 2019 + 10.7303/syn17865732 + + + + + + BrandAmy + AllenLiz + AltmanMicah + HlavaMarjorie + ScottJo + + Beyond authorship: Attribution, contribution, collaboration, and credit + Learned Publishing + 2015 + 28 + 2 + https://onlinelibrary.wiley.com/doi/abs/10.1087/20150211 + 10.1087/20150211 + 151 + 155 + + + + +
diff --git a/joss.06317/10.21105.joss.06317.pdf b/joss.06317/10.21105.joss.06317.pdf new file mode 100644 index 0000000000..4bd12fce91 Binary files /dev/null and b/joss.06317/10.21105.joss.06317.pdf differ diff --git a/joss.06317/media/Overview_New.png b/joss.06317/media/Overview_New.png new file mode 100644 index 0000000000..ad57d259c9 Binary files /dev/null and b/joss.06317/media/Overview_New.png differ