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A New Method for High-Throughput Histo-Cytometry Analysis of Images and Movies

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A New Method for High-Throughput Histo-Cytometry Analysis of Images and Movies Chrysalis: A New Method for High-Throughput Histo-Cytometry Analysis of Images and Movies This information is current as Dmitri I. Kotov, Thomas Pengo, Jason S. Mitchell, Matthew of December 5, 2018. J. Gastinger and Marc K. Jenkins J Immunol published online 3 December 2018 http://www.jimmunol.org/content/early/2018/11/30/jimmun ol.1801202 Downloaded from Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision http://www.jimmunol.org/ • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: by guest on December 5, 2018 http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2018 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published December 3, 2018, doi:10.4049/jimmunol.1801202 The Journal of Immunology Chrysalis: A New Method for High-Throughput Histo-Cytometry Analysis of Images and Movies Dmitri I. Kotov,*,†,1 Thomas Pengo,‡,1 Jason S. Mitchell,*,x,{ Matthew J. Gastinger,‖ and Marc K. Jenkins*,† Advances in imaging have led to the development of powerful multispectral, quantitative imaging techniques, like histo-cytometry. The utility of this approach is limited, however, by the need for time consuming manual image analysis. We therefore developed the software Chrysalis and a group of Imaris Xtensions to automate this process. The resulting automation allowed for high-throughput histo-cytometry analysis of three-dimensional confocal microscopy and two-photon time-lapse images of T cell–dendritic cell interactions in mouse spleens. It was also applied to epi-fluorescence images to quantify T cell localization within splenic tissue by using a “signal absorption” strategy that avoids computationally intensive distance measurements. In summary, this image processing and analysis software makes histo-cytometry more useful for immunology applications by automating image Downloaded from analysis. The Journal of Immunology, 2019, 202: 000–000. maging of biological samples has traditionally been used to images. The broad applicability of this protocol was confirmed by resolve anatomic structures (1) or identify specific cells in quantifying cell localization and cell-cell interactions in the I tissues (2). Recent advances in image analysis, like histo- spleen, using multiple imaging platforms. Automation should fa- cytometry (3) and dynamic in situ cytometry (4) have expanded cilitate the use of the powerful histo-cytometry technique. http://www.jimmunol.org/ the depth of analysis by increasing characterization of cell types and objective quantification of cells in images. These new tech- Materials and Methods niques combine multispectral image analysis with a quantitative Mice workflow. The image quantification is performed by analyzing Six- to eight-wk-old C57BL/6 (B6) female mice were purchased from the image-derived statistics in flow cytometry analysis software (3, 4). Jackson Laboratory or the National Cancer Institute Mouse Repository These approaches can quantify the number and location of cells (Frederick, MD). ItgaxYFP (13) and Rag12/2 UbcGFP (14) TEa TCR throughout a tissue (5), identify cell-cell interactions (6), and transgenic (Tg) (15) female mice were a gift from B.T. Fife (University of 2/2 2/2 correlate protein expression to cellular localization (7). Histo- Minnesota). Rag1 B3K506 TCR TG (16) and Rag1 B3K508 TCR Tg (16) were bred and housed in specific pathogen–free conditions in by guest on December 5, 2018 cytometry and dynamic in situ cytometry have been applied to accordance with guidelines of the University Institutional Animal Care and a variety of imaging systems, including confocal (8–10), epi- Use Committee and National Institutes of Health. The University Institu- fluorescence (11, 12), and two-photon microscopy (4). However, tional Animal Care and Use Committee approved all animal experiments. these approaches are time consuming because of the need for Infections extensive hands-on image processing. We addressed this issue by creating the software Chrysalis and a suite of Imaris Xtensions to Mice were injected i.v. with 1 3 107 CFUs of ActA-deficient Listeria batch image processing and analysis (https://histo-cytometry. monocytogenes expressing the P5R peptide (Lm-P5R) (17, 18). github.io/Chrysalis/). This automation reduced hands-on analysis Cell transfer time for confocal, epi-fluorescence, and two-photon microscopy 2 2 2 2 Lymph nodes were collected from Rag1 / B3K506 TCR Tg, Rag1 / B3K508 TCR Tg, and Rag12/2 UbcGFP TEa TCR Tg mice, and a small sample was stained with allophycocyanin-labeled CD4 Ab (RM4-5; Tonbo *Center for Immunology, University of Minnesota, Minneapolis, MN 55455; Biosciences) and analyzed on an BD LSR II (BD Biosciences) flow † Department of Microbiology and Immunology, University of Minnesota, Minneap- cytometer using FlowJo software (Tree Star). The results were used to olis, MN 55455; ‡University of Minnesota Informatics Institute, University of x calculate the amount of the remaining sample needed to transfer 1 million Minnesota Twin Cities, Minneapolis, MN 55455; University Imaging Centers, + 2/2 { CD4 T cells. In some cases, the T cells from the Rag1 B3K506 and University of Minnesota, Minneapolis, MN 55455; Department of Medicine, 2/2 University of Minnesota, Minneapolis, MN 55455; and ‖Bitplane USA, Concord, Rag1 B3K508 TCR Tg mice were also labeled with CellTracker MA 01742 Orange (Thermo Fisher Scientific) or CellTraceViolet (Thermo Fisher 1 Scientific), respectively (19). One million TCR Tg cells were transferred D.I.K. and T.P. contributed equally to this work. into B6 mice by i.v. injection 24 h prior to infection with Lm-P5R. ORCIDs: 0000-0001-7843-1503 (D.I.K.); 0000-0001-8009-7655 (M.K.J.). Confocal microscopy Received for publication August 31, 2018. Accepted for publication November 2, 2018. Twenty-micrometer splenic sections from naive or Lm-P5R–infected mice This work was supported by National Institutes of Health Grants T32 AI083196 and were stained with Brilliant Violet (BV) 421–conjugated F4/80 (BM8; T32 AI007313 (to D.I.K.) and R01 AI039614 (to M.K.J.). BioLegend), Pacific Blue–conjugated B220 (RA3-6B2; BioLegend), ⍺ Address correspondence and reprint requests to Dmitri I. Kotov, University of CF405L-conjugated CD8 (53-6.7; BioLegend), AF488-conjugated Minnesota, 3-280 Wallin Medical Biosciences Building, 2101 6th Street SE, phosphorylated form of S6 kinase (pS6) (2F9; Cell Signaling Technology), Minneapolis, MN 55455. E-mail address: [email protected] CF555-conjugated CD86 (GL-1; BioLegend), AF647-conjugated Abbreviations used in this article: B6, C57BL/6; BV, Brilliant Violet; DC, dendritic CD45.2 (104; BioLegend), AF700-conjugated MHC class II (MHCII) cell; Lm-P5R, Listeria monocytogenes expressing P5R; MHCII, MHC class II; pS6, (M5/114.15.2; BioLegend), CF514-conjugated CD11c (N418; Bio- phosphorylated form of S6 kinase; Tfh, T follicular helper; Tg, transgenic. Legend), BV480-conjugated CD3 (17A2; BD Biosciences), and AF594- conjugated SIRP⍺ (P84; BioLegend) Abs. Certain purified Abs from Copyright Ó 2018 by The American Association of Immunologists, Inc. 0022-1767/18/$37.50 BioLegend were conjugated with CF405L, CF514, or CF555 with Biotium www.jimmunol.org/cgi/doi/10.4049/jimmunol.1801202 2 AUTOMATION OF QUANTITATIVE IMAGE ANALYSIS USING CHRYSALIS Mix-n-Stain labeling kits. Confocal microscopy was performed with a Statistics were exported for each surface and imported into FlowJo v10.3 Leica TCS SP5 confocal microscope with two HyD detectors; two PMT (Tree Star) for quantitative image analysis. detectors; 405, 458, 488, 514, 543, 594, and 633 laser lines; and a 633 oil objective with a 1.4 numerical aperture. The mark-and-find feature in the Code availability Leica Application Suite was used to image 12 T cell zones in each spleen, with each image consisting of a 20-mm z-stack acquired at a 0.5-mm step All of the code generated for image processing or analysis can be size. Additionally, the Leica TCS SP5 microscope was used to image downloaded at https://histo-cytometry.github.io/Chrysalis/, including single-color–stained UltraComp eBeads (Thermo Fisher Scientific) for compiled versions of Chrysalis for Windows and Mac OSX, with a Linux generating a compensation matrix. version available upon request because of GitHub limitations on file size. Additionally, all of the Imaris Xtensions are compatible with Windows Epi-fluorescence microscopy and Mac OSX. The documentation for the code as well as a detailed protocol for image acquisition and analysis is also provided at this Spleens from B6 mice infected 48 h earlier with Lm-P5R were fixed with GitHub link. paraformaldehyde, dehydrated with sucrose, and embedded in OCT. Seven-micrometer sections of these spleens were stained with BV421- conjugated F4/80, AF488-conjugated B220 (RA3-6B2; BioLegend), Results AF647-conjugated CD45.2 (104; BioLegend), and AF594-conjugated CD3 Automated processing of three-dimensional images (17A2; BioLegend) Abs. The samples were imaged with a Leica DM6000 B epi-fluorescence microscope equipped with a dry 203 objective with 0.5 Image acquisition, processing, and analysis with histo-cytometry numerical aperture and a Leica DFC9000 camera with custom filter cubes. consists of eight steps (Fig. 1A). We developed a stand-alone soft- The tiling feature in the Leica Application Suite (Leica Microsystems) soft- ware called Chrysalis for automating the three image process- ware was used to image the entire splenic section. The images were ana- ing steps (steps 2–4) as well as a suite of Imaris Xtensions that lyzed in Imaris 8.4 (Bitplane), which was used to create surfaces to identify TCR Tg cells.
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