Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer Jia-Ren Lin*, Shu Wang*, Shannon Coy*, Yu-An Chen, Clarence Yapp, Madison Tyler, Maulik K. Nariya, Cody N. Heiser, Ken S. Lau, Sandro Santagata†, and Peter K. Sorger† *These (first) authors contributed equally †These (senior) authors contributed equally Associated publication DOI: 10.1016/J.CELL.2022.12.028 Learn more: tissue-atlas.org/atlas-datasets/lin-wang-coy-2021/ ----- SUMMARY Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T-cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues. ----- VIEW IMAGE DATA ONLINE Some data is available as narrated data explorations (with text and audio narration) for anonymous on-line browsing using MINERVA software (Rashid et al., 2022), which allows users to pan and zoom through the images without requiring any software installation. To view the Minerva stories, please visit tissue-atlas.org/atlas-datasets/lin-wang-coy-2021/#data-explorations. ---- ACCESS THE DATA All images at full resolution, derived image data (e.g., segmentation masks), and cell count tables have been released via the NCI-sponsored repository for Human Tumor Atlas Network (HTAN; humantumoratlas.org/explore). The dataset, consist of 47 CRC1 images (2.1 TB) and CRC2-17 images (4.4 TB), is available through Amazon Web Services S3 at the following locations:
Gene delivery to sensory neurons of the dorsal root ganglion (DRG) offers the prospect of developing new clinical interventions against peripheral nerve diseases and disorders. Here we show that genes can be transferred to rat DRG through lumbar intrathecal injection of delivery vectors into the cerebrospinal fluid. Genes could be transferred to DRG using polyethylenimine (PEI)/DNA complexes, Lipofectamine 2000/DNA complexes, adeno-associated virus vectors, or baculovirus vectors. We also show that nerve growth factor cDNA, delivered through lumbar intrathecal injection of PEI complexes, was able to improve regeneration of transected rat sciatic nerves. These data demonstrate the viability of using an intrathecal gene delivery approach for treating peripheral neuropathies.
Structured illumination microscopy (SIM) achieves doubled spatial resolution through exciting the specimen with high-contrast, high-frequency sinusoidal patterns. Such an illumination pattern can be generated by laser interference or incoherent structured pattern. Opto-electronic devices, such as Spatial Light Modulator (SLM) or Digital Micro-mirror Device (DMD), can provide rapid switch of illumination patterns for SIM. Although DMD is much more cost-effective than SLM, it was previously restricted in association with incoherent light sources. To extend its application with coherent illumination, we model the DMD as a blazed grating, and simulate the effect with DMD pattern changes in SIM. Based on the simulation, we report a fast, high-resolution and cost-efficient SIM with DMD. Our home-built laser interference-based DMD-SIM (LiDMD-SIM) reveals the nuclear pore complex and microtubule in mammalian cells with doubled spatial resolution.
Abstract Background: This study is aimed at developing a framework for the evaluation of healthcare data’s value, so as to provide a tool for data managers in making decisions on data openness. Methods: In this study, the Delphi method was adopted. Firstly, a rudimentary framework was constructed following a literature review and focus group interviews, and an inquiry letter was designed. After handing out the inquiry letters to experts in related areas, the framework was modified according to the feedbacks. This process was repeated until a consensus was reached. Results: For this inquiry 15 experts were invited; whose levels of activeness and authority were relatively high. This research produced a framework for the evaluation of healthcare data with 2 primary criteria, 7 secondary criteria and 21 tertiary criteria, after two rounds in Delphi method. Conclusion: The framework established in this research lays a solid foundation for the identifying and evaluation of healthcare data’s value, and is expected to drive the process of opening of valuable healthcare data.
This article reviewed the literature of Acceptance and Commitment Therapy(ACT),which is one of the most representative therapies in the third wave of cognitive and behavioral therapy.We introduced the philosophy background of ACT,known as functional contextualism,and also the theoretical foundation of ACT named Relational Frame Theory(RFT).Furthermore,the hexagon model of psychopathology and two processes therapeutic model were described.Based on the review of empirical research field and application areas of ACT,the applicability and future development of this therapy under Chinese culture background were discussed.
Abstract Background X‐ray repair cross‐complementary 5 ( XRCC5 ) and 6 ( XRCC6 ) are critical for DNA repair. Few studies have assessed their association with breast cancer risk, and related gene‐environment interactions remain poorly understood. This study aimed to determine the influence of XRCC5 / 6 polymorphisms on breast cancer risk, and their interactions with cigarette smoking, alcohol consumption, and sleep satisfaction. Methods The study included 1039 patients with breast cancer and 1040 controls. Four single‐nucleotide polymorphisms of XRCC5 and two of XRCC6 were genotyped. Information about smoking, alcohol consumption, and sleep satisfaction was collected through questionnaires. Odds ratios (OR) and related 95% confidence intervals (95% CI) were assessed using unconditional logistic regression models. Gene‐environment interactions were analyzed using logistic regression with multiplicative interaction models. Results XRCC5 rs16855458 was associated with increased breast cancer risk in the co‐dominant ( p trend = 0.003) and dominant (CA + AA vs. CC, OR = 1.29, 95% CI = 1.07–1.56, p = 0.008) genetic models after Bonferroni correction. The CG + GG genotype of XRCC6 rs2267437 was associated with an increased risk of estrogen receptor‐negative/progesterone receptor‐negative (ER−/PR−) breast cancer (CG + GG vs. CC: OR = 1.54, 95% CI = 1.12–2.13, p = 0.008) after Bonferroni correction. Moreover, an antagonistic interaction between XRCC5 rs16855458 and alcohol consumption ( p interaction = 0.017), and a synergistic interaction between XRCC6 rs2267437 and sleep satisfaction were associated with breast cancer risk ( p interaction = 0.0497). However, these interactions became insignificant after Bonferroni correction. Conclusion XRCC5 rs16855458 was associated with breast cancer risk, and XRCC6 rs2267437 was associated with the risk of ER−/PR− breast cancer. Breast cancer risk associated with XRCC5 and XRCC6 polymorphisms might vary according to alcohol consumption and sleep satisfaction, respectively, and merit further investigation.
In 2012, the cumulative mortality of farmed sturgeons in Beijing was almost 60% with various symptoms, including the reddening of the anus with yellow exudation, ascities in the peritoneal cavity, petechial haemorrhages in liver and internal muscle wall, and the swollen spleen.We isolated the pathogen from the dying sturgeons with significant pathological signs, and then analyzed its morphological, physiological and biochemical characteristics, taxonomic status, and drug sensitivity. Moreover, the pathogenic characteristic of presumptive pathogens was identified by artificial infection.The 16S rDNA sequence of the pathogen was more than 99% homology with that of Plesiomonas shigelloides, suggesting that the pathogen was P. shigelloides, which was also demonstrated by the results of biochemical tests. The LD50 of the pathogen to sturgeon was 1.0 x 10(5.8) CFU/mL, and it also can cause liver, kidey and spleen to lesions. There were no activities of amylase, caseinase, lipase, gelatinase and haemolysis of extracellular products of P. shigelloides, and its toxicity might be from endotoxin. In addition, the bacterium was specific sensitive to enrofloxacin, doxycyline hyclate, florfenicol and thiamphenicol with MIC less than 2 microg/mL.P. shigelloides was the main pathogen to cultured sturgeons in Beijing area, and enrofloxacin, doxycyline hyclate and florfenicol can be used against the disease.
Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract The architecture of normal and diseased tissues strongly influences the development and progression of disease as well as responsiveness and resistance to therapy. We describe a tissue-based cyclic immunofluorescence (t-CyCIF) method for highly multiplexed immuno-fluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides, the most widely used specimens for histopathological diagnosis of cancer and other diseases. t-CyCIF generates up to 60-plex images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high-dimensional representation. t-CyCIF requires no specialized instruments or reagents and is compatible with super-resolution imaging; we demonstrate its application to quantifying signal transduction cascades, tumor antigens and immune markers in diverse tissues and tumors. The simplicity and adaptability of t-CyCIF makes it an effective method for pre-clinical and clinical research and a natural complement to single-cell genomics. https://doi.org/10.7554/eLife.31657.001 eLife digest To diagnose a disease such as cancer, doctors sometimes take small tissue samples called biopsies from the affected area. These biopsies are then thinly sliced and treated with dyes to identify healthy and cancerous cells. However, clinicians and scientists often need to look into what happens inside individual cells in the tissues so they can understand how cancers arise and progress. This helps them to identify different types of tumor cells and to tailor the best treatment for the patient. To do so, a number of proteins (the molecules involved in nearly all life’s processes) need to be tracked in healthy and diseased cells and tissues. This can be done thanks to a range of methods known as immunofluorescence microscopy, but following different proteins on the same slice of a sample is difficult. However, a new type of immunofluorescence known as t-CyCIF may be a solution. With this technique, a fluorescent compound is applied that will bind to a specific protein of interest. A microscope can pick up the light from the compound when the sample is imaged, which reveals the protein’s location in the cell or tissue. Then, a substance is used that deactivates the fluorescence signal. After this, another compound that binds to a new type of protein is used, and imaged. This cycle is repeated several times to locate different proteins. Lastly, the individual images are processed and stitched together to reveal the cells and their internal structures. Here, Lin, Izar et al. showed that t-CyCIF could be used to study biopsies and to obtain images that covered a large area of healthy human tissues and tumors. The technique helped to track over 60 different proteins in normal and tumor tissue samples from human patients. Several sets of experiments showed that t-CyCIF could uncover the molecular mechanisms that are disrupted during cancer, but also reveal the complexity of a single tumor. In fact, as shown with biopsies of brain cancer, cancerous cells in a tumor can be strikingly different, even when they are close to each other. Finally, the method helped to pinpoint which types of immune cells are involved in fighting a kidney tumor. Overall, such information cannot be obtained with conventional methods, yet is crucial for diagnosis and treatment. Most laboratories can readily use t-CyCIF since the technique is open source and requires equipment that is easily accessible. In fact, the technique should soon be used to assess how well certain drugs help the immune system combat cancer. Ultimately, better use of biopsies is key to customizing cancer care. https://doi.org/10.7554/eLife.31657.002 Introduction Histopathology is among the most important and widely used methods for diagnosing human disease and studying the development of multicellular organisms. As commonly performed, imaging of formalin-fixed, paraffin-embedded (FFPE) tissue has relatively low dimensionality, primarily comprising Hematoxylin and Eosin (H&E) staining supplemented by immunohistochemistry (IHC). The potential of IHC to aid in diagnosis and prioritization of therapy is well established (Bodenmiller, 2016), but IHC is primarily a single-channel method: imaging multiple antigens usually involves the analysis of sequential tissue slices or harsh stripping protocols (although limited multiplexing is possible using IHC and bright-field imaging [Stack et al., 2014; Tsujikawa et al., 2017]). Antibody detection via formation of a brown diamino-benzidine (DAB) or similar precipitates are also less quantitative than fluorescence (Rimm, 2006). The limitations of IHC are particularly acute when it is necessary to quantify complex cellular states and multiple cell types, such as tumor infiltrating regulatory and cytotoxic T cells (Postow et al., 2015) in parallel with tissue and pharmaco-dynamic markers. Advances in DNA and RNA profiling have dramatically improved our understanding of oncogenesis and propelled the development of targeted anticancer drugs (Garraway and Lander, 2013). Sequence data are particularly useful when an oncogenic driver is both a drug target and a biomarker of drug response, such as BRAFV600E in melanoma (Chapman et al., 2011) or BCR-ABL in chronic myelogenous leukemia (Druker and Lydon, 2000). However, in the case of drugs that act through cell non-autonomous mechanisms, such as immune checkpoint inhibitors, tumor-drug interaction must be studied in the context of multicellular environments that include both cancer and non-malignant stromal and infiltrating immune cells. Multiple studies have established that these components of the tumor microenvironment strongly influence the initiation, progression and metastasis of cancer (Hanahan and Weinberg, 2011) and the magnitude of responsiveness or resistance to immunotherapies (Tumeh et al., 2014). Single-cell transcriptome profiling provides a means to dissect tumor ecosystems at a molecular level and quantify cell types and states (Tirosh et al., 2016). However, single-cell sequencing usually requires disaggregation of tissues, resulting in loss of spatial context (Tirosh et al., 2016; Patel et al., 2014). As a consequence, a variety of multiplexed approaches to analyzing tissues have recently been developed with the goal of simultaneously assaying cell identity, state, and morphology (Giesen et al., 2014; Gerdes et al., 2013; Micheva and Smith, 2007; Remark et al., 2016; Gerner et al., 2012). For example, FISSEQ (Lee et al., 2014) enables genome-scale RNA profiling of tissues at single-cell resolution, and multiplexed ion beam imaging (MIBI) and imaging mass cytometry achieve a high degree of multiplexing using antibodies as reagents, metals as labels and mass spectrometry as a detection modality (Giesen et al., 2014; Angelo et al., 2014). Despite the potential of these new methods, they require specialized instrumentation and consumables, which is one reason that the great majority of basic and clinical studies still rely on H&E and single-channel IHC staining. Moreover, methods that involve laser ablation of samples such as MIBI inherently have a lower resolution than optical imaging. Thus, there remains a need for highly multiplexed tissue analysis methods that (i) minimize the requirement for specialized instruments and costly, proprietary reagents, (ii) work with conventionally prepared FFPE tissue specimens collected in clinical practice and research settings, (iii) enable imaging of ca. 50 antigens at subcellular resolution across a wide range of cell and tumor types, (iv) collect data with sufficient throughput that large specimens (several square centimeters) can be imaged and analyzed, (v) generate high-resolution data typical of optical microscopy, and (vi) allow investigators to customize the antibody mix to specific questions or tissue types. Among these requirements the last is particularly critical: at the current early stage of development of high dimensional histology, it is essential that individual research groups be able to test the widest possible range of antibodies and antigens in search of those with the greatest scientific and diagnostic value. This paper describes a method for highly multiplexed fluorescence imaging of tissues, tissue-based cyclic immunofluorescence (t-CyCIF), inspired by a cyclic method first described by Gerdes et al. (2013). t-CyCIF also extends a method we previously described for imaging cells grown in culture (Lin et al., 2015). In its current implementation, t-CyCIF assembles up to 60-plex images of FFPE tissue sections via successive rounds of four-channel imaging. t-CyCIF uses widely available reagents, conventional slide scanners and microscopes, manual or automated slide processing and simple protocols. It can, therefore, be implemented in most research or clinical laboratories on existing equipment. Our data suggest that high-dimensional imaging methods using cyclic immunofluorescence have the potential to become a robust and widely-used complement to single-cell genomics, enabling routine analysis of tissue and cancer morphology and phenotypes at single-cell resolution. Results t-CyCIF enables multiplexed imaging of FFPE tissue and tumor specimens at subcellular resolution Cyclic immunofluorescence (Gerdes et al., 2013) creates highly multiplexed images using an iterative process (a cycle) in which conventional low-plex fluorescence images are repeatedly collected from the same sample and then assembled into a high-dimensional representation. In the implementation described here, samples ~5 µm thick are cut from FFPE blocks, the standard in most histopathology services, followed be dewaxing and antigen retrieval either manually or on automated slide strainers in the usual manner (Shi et al., 2011). To reduce auto-fluorescence and non-specific antibody binding, a cycle of ‘pre-staining’ is performed; this involves incubating the sample with secondary antibodies followed by fluorophore oxidation in a high pH hydrogen peroxide solution in the presence of light (‘fluorophore bleaching’). Subsequent t-CyCIF cycles each involve four steps (Figure 1A): (i) immuno-staining with antibodies against protein antigens (three antigens per cycle in the implementation described here) (ii) staining with a DNA dye (commonly Hoechst 33342) to mark nuclei and facilitate image registration across cycles (iii) four-channel imaging at low- and high-magnification (iv) fluorophore bleaching followed by a wash step and then another round of immuno-staining. In t-CyCIF, the signal-to-noise ratio often increases with cycle number due to progressive reductions in background intensity over the course of multiple rounds of fluorophore bleaching. This effect is visible in Figure 1B as the gradual disappearance of an auto-fluorescent feature (denoted by a dotted white oval and quantified in Figure 1—figure supplement 1; see detailed analysis below). When no more t-CyCIF cycles are to be performed, the specimen is stained with H&E to enable conventional histopathology review. Individual image panels are stitched together and registered across cycles followed by image processing and segmentation to identify cells and other structures. t-CyCIF allows for one cycle of indirect immunofluorescence using secondary antibodies. In all other cycles antibodies are directly conjugated to fluorophores, typically Alexa 488, 555 or 647 (for a description of different modes of CyCIF see Lin et al., 2015). As an alternative to chemical coupling we have tested the Zenon antibody labeling method (Tang et al., 2010) from ThermoFisher in which isotype-specific Fab fragments pre-labeled with fluorophores are bound to primary antibodies to create immune complexes; the immune complexes are then incubated with tissue samples (Figure 1—figure supplement 2). This method is effective with 30–40% of the primary antibodies that we have tested and potentially represents a simple way to label a wide range of primary antibodies with different fluorophores. Figure 1 with 2 supplements see all Download asset Open asset Steps in the t-CyCIF process. (A) Schematic of the cyclic process whereby t-CyCIF images are assembled via multiple rounds of four-color imaging. (B) Image of human tonsil prior to pre-staining and then over the course of three rounds of t-CyCIF. The dashed circle highlights a region with auto-fluorescence in both green and red channels (used for Alexa-488 and Alexa-647, respectively) and corresponds to a strong background signal. With subsequent inactivation and staining cycles (three cycles shown here), this background signal becomes progressively less intense; the phenomenon of decreasing background signal and increasing signal-to-noise ratio as cycle number increases was observed in several staining settings (see also Figure 1—figure supplement 1). https://doi.org/10.7554/eLife.31657.003 Imaging of t-CyCIF samples can be performed on a variety of fluorescent microscopes each of which represent a different tradeoff between data acquisition time, image resolution and sensitivity (Table 1). Greater resolution (a higher numerical aperture objective lens) typically corresponds to a smaller field of view and thus, longer acquisition time for large specimens. Imaging of specimens several square centimeters in area at a resolution of ~1 µm is routinely performed on microscopes specialized for scanning slides (slide scanners); we use a CyteFinder system from RareCyte (Seattle WA) configured with 10 × 0.3 NA and 40 × 0.6 NA objectives but have tested scanners from Leica, Nikon and other manufacturers. Figure 2A–B show an H&E image of a ~10 × 11 mm metastatic melanoma specimen and a t-CyCIF image assembled from 165 individual image tiles. The assembly process involves stitching sequential image tiles from a single t-CyCIF cycle into one large image panel, flat-fielding to correct for uneven illumination and registration of images from successive t-CyCIF cycles to each other; these procedures were performed using ImageJ, ASHLAR, and BaSiC software as described in materials and methods (Peng et al., 2017). Figure 2 with 2 supplements see all Download asset Open asset Multi-scale imaging of t-CyCIF specimens. (A) Bright-field H&E image of a metastasectomy specimen that includes a large metastatic melanoma lesion and adjacent benign tissue. The H&E staining was performed after the same specimen had undergone t-CyCIF. (B) Representative t-CyCIF staining of the specimen shown in (A) stitched together using the Ashlar software from 165 successive CyteFinder fields using a 20X/0.8NA objective. (C) One field from (B) at the tumor-normal junction demonstrating staining for S100-postive malignant cells, α-SMA positive stroma, T lymphocytes (positive for CD3, CD4 and CD8), and the proliferation marker phospho-RB (pRB). (D) A melanoma tumor imaged on a GE INCell Analyzer 6000 confocal microscope to demonstrate sub-cellular and sub-organelle structures. This specimen was stained with phospho-Tyrosine (pTyr), Lamin A/C and p-Aurora A/B/C and imaged with a 60X/0.95NA objective. pTyr is localized in membrane in patches associated with receptor-tyrosine kinase, visible here as red punctate structures. Lamin A/C is a nuclear membrane protein that outlines the vicinity of the cell nucleus in this image. Aurora kinases A/B/C coordinate centromere and centrosome function and are visible in this image bound to chromosomes within a nucleus of a mitotic cell in prophase (yellow arrow). (E) Staining of a melanoma sample using the GE OMX Blaze structured illumination microscope with a 60X/1.42NA objective shows heterogeneity of structural proteins of the nucleus, including as Lamin B and Lamin A/C (indicated by yellow arrows) and part of the nuclear pore complex (NUP98) that measures ~120 nm in total size and indirectly allows the visualization of nuclear pores (indicated by non-continuous staining of NUP98). (F) Staining of a patient-derived mouse xenograft breast tumor using the OMX Blaze with a 60x/1.42NA objective shows a spindle in a mitotic cell (beta-tubulin in red) as well as vesicles staining positive for VEGFR2 (in cyan) and punctuate expression of the EGFR in the plasma membrane (in green). https://doi.org/10.7554/eLife.31657.006 Table 1 Microscopes used in this study and their properties. https://doi.org/10.7554/eLife.31657.009 InstrumentTypeObjectiveField of viewNominal Resolution*RareCyte CytefinderSlide Scanner10X/0.3 NA1.6 × 1.4 mm1.06 µm20X/0.8NA0.8 × 0.7 mm0.40 µm40X/0.6 NA0.42 × 0.35 mm0.53 µmGE INCell Analyzer 6000Confocal60X/0.95 NA0.22 × 0.22 mm0.21 µmGE OMX BlazeStructured Illumination Microscope60 × 1.42 NA0.08 × 0.08 mm0.11 µm *Except in the case of the OMX Blaze, nominal resolution was calculated using the formula (r) = 0.61λ/NA for widefield and (r) = 0.4λ/NA for confocal microscopy with λ = 520 nm. Actual resolution depends on optical properties and thickness of sample, alignment and quality of the optical components in the light path. For structured illumination microscopy, actual resolution depends on accurate matching of immersion oil refractive index with sample in the Cy3 channel and use of an optimal point spread function during reconstruction process. The resolution in other channels will be sub-nominal. In the t-CyCIF image (Figure 2B) tumor cells staining positive for S100 (a melanoma marker in green [Henze et al., 1997]) are surrounded by CD45-positive immune cells (CD45RO+ cells in white) and by stromal cells expressing the alpha isoform of smooth muscle actin (α-SMA in red). By zooming in on one tile, single cells can be identified and characterized (Figure 2C); in this image, CD4+ and CD8+ T-lymphocytes and proliferating pRB+ positive cells are visible. At 60X resolution on a confocal GE INCell Analyzer 6000, kinetochores stain positive for the phosphorylated form of the Aurora A/B/C kinase and can be counted in a mitotic cell (yellow arrowhead in Figure 2D). Nominally super-resolution imaging on a GE OMX Blaze Structured Illumination Microscope (Carlton et al., 2010) (using a 60 × 1.42 Plan Apo objective) reveals very fine structural details including differential expression of Lamin isotypes (in a melanoma, Figure 2E and Figure 2—figure supplement 2) and mitotic spindle fibers (in cells of a xenograft tumor; Figure 2F and Figure 2—figure supplement 2). These data show that t-CyCIF images have readily interpretable features at the scale of an entire tumor, individual tumor cells and subcellular structures. Little subcellular (or super-resolution) imaging of clinical FFPE specimens has been reported to date (but see Chen et al., 2015), but fine subcellular morphology has the potential to provide dramatically greater information than simple integration of antibody intensities across whole cells. To date, we have tested commercial antibodies against ~200 different proteins for their compatibility with t-CyCIF; these include lineage makers, cytoskeletal proteins, cell cycle regulators, the phosphorylated forms of signaling proteins and kinases, transcription factors, markers of cell state including quiescence, senescence, apoptosis, stress, etc. as well as a variety of non-antibody-based fluorescent stains (Table 2). Multiplexing antibodies and stains makes it possible to discriminate among proliferating, quiescent and dying cells, identify tumor and stroma, and collect immuno-phenotypes (Angelo et al., 2014; Giesen et al., 2014; Goltsev, 2017). Use of phospho-specific antibodies and antibodies against proteins that re-localize upon activation (e.g. transcription factors) makes it possible to assay the states of signal transduction networks. For example, in a 10-cycle t-CyCIF analysis of human tonsil (Figure 3A) subcellular features such as membrane staining, Ki-67 puncta (Cycle 1), ring-like staining of the nuclear lamina (Cycle 6) and nuclear exclusion of NF-ĸB (Cycle 6) can easily be demonstrated (Figure 3B). The five-cycle t-CyCIF data on normal skin in Figure 3C shows tight localization of auto-fluorescence (likely melanin) to the epidermis prior to pre-bleaching and images of three non-antibody stains used in the last t-CyCIF cycle: HCS CellMask Red Stain for cytoplasm and nuclei, Actin Red, a Phalloidin-based stain for actin and Mito-tracker Green for mitochondria. Figure 3 Download asset Open asset t-CyCIF imaging of normal tissues. (A) Selected images of a tonsil specimen subjected to 10-cycle t-CyCIF to demonstrate tissue, cellular, and subcellular localization of tissue and immune markers (see Supplementary file 1 for a list of antibodies). (B) Selected cycles from (A) demonstrating sub-nuclear features (Ki67 staining, cycle 1), immune cell distribution (cycle 2), structural proteins (E-Cadherin and Vimentin, cycle 5) and nuclear vs. cytosolic localization of transcription factors (NF-kB, cycle 6). (C) Five-cycle t-CyCIF of human skin to show the tight localization of some auto-fluorescence signals (Cycle 0), the elimination of these signals after pre-staining (Cycle 1), and the dispersal of rare cell types within a complex layered tissue (see Supplementary file 1 for a list of the antibodies). https://doi.org/10.7554/eLife.31657.010 Table 2 List of antibodies tested and validated for t-CyCIF. https://doi.org/10.7554/eLife.31657.011 Antibody nameTarget proteinPerformanceVendorCatalog no.CloneFluorophoreResearch resource IdentifierBax-488Bax*BioLegend6336032D2Alexa Fluor 488AB_2562171CD11b-488CD11b*AbcamAB204271EPR1344Alexa Fluor 488CD4-488CD4*R and D SystemsFAB8165GPolyclonalAlexa Fluor 488CD8a-488CD8*eBioscience53-0008-80AMC908Alexa Fluor 488AB_2574412cJUN-488cJUN*AbcamAB193780E254Alexa Fluor 488CK18-488Cytokeratin 18*eBioscience53-9815-80LDK18Alexa Fluor 488AB_2574480CK8-FITCCytokeratin 8*eBioscience11-9938-80LP3KFITCAB_10548518CycD1-488CycD1*AbcamAB190194EPR2241Alexa Fluor 488Ecad-488E-Cadherin*CST319924E10Alexa Fluor 488AB_10691457EGFR-488EGFR*CST5616D38B1Alexa Fluor 488AB_10691853EpCAM-488EpCAM*CST5198VU1D9Alexa Fluor 488AB_10692105HES1-488HES1*AbcamAB196328EPR4226Alexa Fluor 488Ki67-488Ki67*CST11882D3B5Alexa Fluor 488AB_2687824LaminA/C-488Lamin A/C*CST86174C11Alexa Fluor 488AB_10997529LaminB1-488Lamin B1*AbcamAB194106EPR8985(B)Alexa Fluor 488mCD3E-FITCms_CD3E*BioLegend100306145–2 C11FITCAB_312671mCD4-488ms_CD4*BioLegend100532RM4-5Alexa Fluor 488AB_493373MET-488c-MET*CST8494D1C2Alexa Fluor 488AB_10999405mF4/80-488ms_F4/80*BioLegend123120BM8Alexa Fluor 488AB_893479MITF-488MITF*AbcamAB201675D5Alexa Fluor 488Ncad-488N-Cadherin*BioLegend3508098C11Alexa Fluor 488AB_11218797p53-488p53*CST54297F5Alexa Fluor 488AB_10695458PCNA-488PCNA*CST8580PC10Alexa Fluor 488AB_11178664PD1-488PD1*CST15131D3W4UAlexa Fluor 488PDI-488PDI*CST5051C81H6Alexa Fluor 488AB_10950503pERK-488pERK(T202/Y204)*CST4344D13.14.4EAlexa Fluor 488AB_10695876pNDG1-488pNDG1(T346)*CST6992D98G11Alexa Fluor 488AB_10827648POL2A-488POL2A*Novus BiologicalsNB200-598AF4884H8Alexa Fluor 488AB_2167465pS6(S240/244)−488pS6(240/244)*CST5018D68F8Alexa Fluor 488AB_10695861S100a-488S100alpha*AbcamAB207367EPR5251Alexa Fluor 488SQSTM1-488SQSTM1/p62*CST8833D1D9E3Alexa Fluor 488STAT3-488STAT3*CST14047B3Z2GAlexa Fluor 488Survivin-488Survivin*CST281071G4B7Alexa Fluor 488AB_10691462Catenin-488β-Catenin*CST2849L54E2Alexa Fluor 488AB_10693296Actin-555Actin*CST804613E5Alexa Fluor 555AB_11179208CD11c-570CD11c*eBioscience41-9761-80118/A5eFluor 570AB_2573632CD3D-555CD3D*AbcamAB208514EP4426Alexa Fluor 555CD4-570CD4*eBioscience41-2444-80N1UG0eFluor 570AB_2573601CD45-PECD45*R and D SystemsFAB1430P-1002D1PEAB_2237898CK7-555Cytokeratin 7*AbcamAB209601EPR17078Alexa Fluor 555cMYC-555cMYC*AbcamAB201780Y69Alexa Fluor 555E2F1-555E2F1*AbcamAB208078EPR3818(3)Alexa Fluor 555Ecad-555E-Cadherin*CST429524E10Alexa Fluor 555EpCAM-PEEpCAM*BioLegend3242059C4PEAB_756079FOXO1a-555FOXO1a*AbcamAB207244EP927YAlexa Fluor 555FOXP3-570FOXP3*eBioscience41-4777-80236A/E7eFluor 570AB_2573608GFAP-570GFAP*eBioscience41-9892-80GA5eFluor 570AB_2573655HSP90-PEHSP90b*AbcamAB115641PolyclonalPEAB_10936222KAP1-594KAP1*BioLegend61930420A1Alexa Fluor 594AB_2563298Keratin-555pan-Keratin*CST3478C11Alexa Fluor 555AB_10829040Keratin-570pan-Keratin*eBioscience41-9003-80AE1/AE3eFluor 570AB_11217482Ki67-570Ki67*eBioscience41-5699-8020Raj1eFluor 570AB_11220088LC3-555LC3*CST13173D3U4CAlexa Fluor 555MAP2-570MAP2*eBioscience41-9763-80AP20eFluor 570AB_2573634pAUR-555pAUR1/2/3(T288/T2*CST13464D13A11Alexa Fluor 555pCHK2-PEpChk2(T68)*CST12812C13C1PEPDL1-555PD-L1/CD274*AbcamAB21335828–8Alexa Fluor 555pH3-555pH3(S10)*CST3475D2C8Alexa Fluor 555AB_10694639pRB-555pRB(S807/811)*CST8957D20B12Alexa Fluor 555pS6(235/236)–555pS6(235/236)*CST3985D57.2.2EAlexa Fluor 555AB_10693792pSRC-PEpSRC(Y418)*eBioscience12-9034-41SC1T2M3PEAB_2572680S6-555S6*CST698954D2Alexa Fluor 555AB_10828226SQSTM1-555SQSTM1/p62*AbcamAB203430EPR4844Alexa Fluor 555VEGFR2-555VEGFR2*CST12872D5B1Alexa Fluor 555VEGFR2-PEVEGFR2*CST12634D5B1PEVimentin-555Vimentin*CST9855D21H3Alexa Fluor 555AB_10859896Vinculin-570Vinculin*eBioscience41-9777-807F9eFluor 570AB_2573646gH2ax-PEgH2ax*BioLegend6134122F3PEAB_2616871AKT-647AKT*CST5186C67E7Alexa Fluor 647AB_10695877aSMA-660aSMA*eBioscience50-9760-801A4eFluor 660AB_2574361B220-647CD45R/B220*BioLegend103226RA3-6B2Alexa Fluor 647AB_389330Bcl2-647Bcl2*BioLegend658705100Alexa Fluor 647AB_2563279Catenin-647Beta-Catenin*CST4627L54E2Alexa Fluor 647AB_10691326CD20-660CD20*eBioscience50-0202-80L26eFluor 660AB_11151691CD45-647CD45*BioLegend304020HI30Alexa Fluor 647AB_493034CD8a-660CD8*eBioscience50-0008-80AMC908eFluor 660AB_2574148CK5-647Cytokeratin 5*AbcamAB193895EP1601YAlexa Fluor 647CoIIV-647Collagen IV*eBioscience51-9871-801042Alexa Fluor 647AB_10854267COXIV-647COXIV*CST75613E11Alexa Fluor 647AB_10994876cPARP-647cPARP*CST6987D64E10Alexa Fluor 647AB_10858215FOXA2-660FOXA2*eBioscience50-4778-823C10eFluor 660AB_2574221FOXP3-647FOXP3*BioLegend320113206DAlexa Fluor 647AB_439753gH2ax-647H2ax(S139)*CST972020E3Alexa Fluor 647AB_10692910gH2ax-647H2ax(S139)*BioLegend6134072F3Alexa Fluor 647AB_2114994HES1-647HES1*AbcamAB196577EPR4226Alexa Fluor 647Ki67-647Ki67*CST12075D3B5Alexa Fluor 647Ki67-647Ki67*BioLegend350509Ki-67Alexa Fluor 647AB_10900810mCD45-647ms_CD45*BioLegend10312430-F11Alexa Fluor 647AB_493533mCD4-647ms_CD4*BioLegend100426GK1.5Alexa Fluor 647AB_493519mEPCAM-647ms_EPCAM*BioLegend118211G8.8Alexa Fluor 647AB_1134104MHCI-647MHCI/HLAA*AbcamAB199837EP1395YAlexa Fluor 647MHCII-647MHCII*AbcamAB201347EPR11226Alexa Fluor 647mLy6C-647ms_Ly6C*BioLegend128009HK1.4Alexa Fluor 647AB_1236551mTOR-647mTOR*CST50487C10Alexa Fluor 647AB_10828101NFkB-647NFkB (p65)*AbcamAB190589E379Alexa Fluor 647NGFR-647NGFR/CD271*AbcamAB195180EP1039YAlexa Fluor 647NUP98-647NUP98*CST13393C39A3Alexa Fluor 647p21-647p21*CST858712D1Alexa Fluor 647AB_10892861p27-647p27*AbcamAB194234Y236Alexa Fluor 647pATM-660pATM(S1981)*eBioscience50-9046-4110H11.E12eFluor 660AB_2574312PAX8-647PAX8*AbcamAB215953EPR18715Alexa Fluor 647PDL1-647PD-L1/CD274*CST15005E1L3NAlexa Fluor 647pMK2-647pMK2(T334)*CST432027B7Alexa Fluor 647AB_10695401pmTOR-660pmTOR(S2448)*eBioscience50-9718-41MRRBYeFluor 660AB_2574351pS6_235–647pS6(S235/S236)*CST4851D57.2.2EAlexa Fluor 647AB_10695457pSTAT3-647pSTAT3(Y705)*CST4324D3A7Alexa Fluor 647AB_10694637pTyr-647p-Tyrosine*CST9415p-Tyr-100Alexa Fluor 647AB_10693160S100A4-647S100A4*AbcamAB196168EPR2761(2)Alexa Fluor 647Survivin-647Survivin*CST286671G4B7Alexa Fluor 647AB_10698609TUBB3-647TUBB3*BioLegend657405AA10Alexa Fluor 647AB_2563609Tubulin-647beta-Tubulin*CST36249F3Alexa Fluor 647AB_10694204Vimentin-647Vimentin*BioLegend677807O91D3Alexa Fluor 647AB_2616801anti-14-3-314-3-3*Santa CruzSC-629-GPolyclonalN/DAB_630820anti-53BP153BP1*BethylA303-906APolyclonalN/DAB_2620256anti-5HMC5HMC*Active Motif39769PolyclonalN/DAB_10013602anti-CD11bCD11b*AbcamAB133357EPR1344N/DAB_2650514anti-CD2CD2*AbcamAB37212PolyclonalN/DAB_726228anti-CD20CD20*DakoM0755L26N/DAB_2282030anti-CD3CD3*DakoA0452PolyclonalN/DAB_2335677anti-CD4CD4*DakoM73104B12N/Danti-CD45ROCD45RO*DakoM0742UCHL1N/DAB_2237910anti-CD8CD8*DakoM7103C8/144BN/DAB_2075537anti-CycA2CycA2*AbcamAB38E23.1N/DAB_304084anti-ET1ET-1*AbcamAB2786TR.ET.48.5N/DAB_303299anti-FAPFAP*eBioscienceBMS168F11-24N/DAB_10597443anti-FOXP3FOXP3*BioLegend320102206DN/DAB_430881anti-LAMP2LAMP2*AbcamAB25631H4B4N/DAB_470709anti-MCM6MCM6*Santa CruzSC-9843PolyclonalN/DAB_2142543anti-PAX8PAX8*AbcamAB191870EPR18715N/Danti-PD1PD1*CST86163D4W2JN/Danti-pEGFRpEGFR(Y1068)*CST3777D7A5N/DAB_2096270anti-pERKpERK(T202/Y204)*CST4370D13.14.4EN/DAB_2315112anti-pRBpRB(S807/811)*Santa CruzSC-16670PolyclonalN/DAB_655250anti-pRPA32pRPA32 (S4/S8)*BethylIHC-00422PolyclonalN/DAB_1659840anti-pSTAT3pSTAT3**CST9145D3A7N/DAB_2491009anti-pTyrpTyr*CST9411p-Tyr-100N/DAB_331228anti-RPA32RPA32*BethylIHC-00417PolyclonalN/DAB_1659838anti-TPCN2TPCN2*NOVUSBIONBP1-86923PolyclonalN/DAB_11021735anti-VEGFR1VEGFR1/FLT1*Santa CruzSC-31173PolyclonalN/DAB_2106885Abeta-488Beta-Amyloid (1-16)†BioLegend8030136E10Alexa Fluor 488AB_2564765BRAF-FITCB-RAF†Abcamab175637K21-FFITCBrdU-488BrdU†BioLegend3641053D4Alexa Fluor 488AB_2564499cCasp3-488cCasp3†R and D SystemsIC835G-025269518Alexa Fluor 488CD11b-488CD11b†BioLegend101219M1/70Alexa Fluor 488AB_493545CD123-488CD123†BioLegend3060356H6Alexa Fluor 488AB_2629569CD49b-FITCCD49b†BioLegend3
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