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Supplementary Figure 1. SPADE plots of normal bone marrow sample no. 6. Supplementary Figure 2. The frequency of cells in the indicated (manually-gated) populations. Supplementary Figure 3. Multi-dimensional binning analysis of the CD34+CD38 low subset confirmed that distinct karyotype and genotype-specific immunophenotypic patterns are observed in high-dimensional space. Supplementary Figure 4. AML subtypes are characterized by distinct phosphoprotein signaling patterns. Supplementary Figure 5. viSNE analysis of CD34+CD38 low subset demonstrates signaling aberrancy is most pronounced in cells outside the viSNE space occupied by normal cells. Supplementary Figure 6. Median basal intracellular signaling of CD34+CD38low cells from AML samples of each subtype. Supplementary Figure 7. HU treatment does not increase the fraction of apoptotic cells measured by cPARP levels. Supplementary Figure 8. Histone 3 lysine 9 acetylation decreases with immunophenotypic differentiation in both normal and AML patient samples.

Supplementary Table 1. Clinical characteristics of AML patients tested in this study. Supplementary Table 2. Antibodies used in this study. Supplementary Table 3. Comparison of clinical (fluorescent) flow cytometry (on left), to mass cytometry analsyis (on right).

Supplementary Figure 9. Gating strategy is shown for each population.

Supplementary Table 5. Number of cell events collected for each sample.

Supplementary Figure Legends

Supplementary Table 4. The raw median ion counts (per cell) for each marker (table heading) in each manually gated immunophenotypic population (columns) of each patient sample (rows) are shown.

Supplementary Methods

Advances in multiplexed imaging technologies have drastically improved our ability to characterize healthy and diseased tissues at the single-cell level. Co-detection by indexing (CODEX) relies on DNA-conjugated antibodies and the cyclic addition and removal of complementary fluorescently labeled DNA probes and has been used so far to simultaneously visualize up to 60 markers in situ. CODEX enables a deep view into the single-cell spatial relationships in tissues and is intended to spur discovery in developmental biology, disease and therapeutic design. Herein, we provide optimized protocols for conjugating purified antibodies to DNA oligonucleotides, validating the conjugation by CODEX staining and executing the CODEX multicycle imaging procedure for both formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissues. In addition, we describe basic image processing and data analysis procedures. We apply this approach to an FFPE human tonsil multicycle experiment. The hands-on experimental time for antibody conjugation is ~4.5 h, validation of DNA-conjugated antibodies with CODEX staining takes ~6.5 h and preparation for a CODEX multicycle experiment takes ~8 h. The multicycle imaging and data analysis time depends on the tissue size, number of markers in the panel and computational complexity. This protocol describes co-detection by indexing, a highly multiplexed imaging technology that uses DNA-conjugated antibodies to image up to 60 markers in formalin-fixed, paraffin-embedded and fresh-frozen tissues.

Multiparameter tissue imaging enables analysis of cell‐cell interactions in situ, the cellular basis for tissue structure, and novel cell types that are spatially restricted, giving clues to biological mechanisms behind tissue homeostasis and disease. Here, we streamlined and simplified the multiplexed imaging method CO‐Detection by indEXing (CODEX) by validating 58 unique oligonucleotide barcodes that can be conjugated to antibodies. We showed that barcoded antibodies retained their specificity for staining cognate targets in human tissue. Antibodies were visualized one at a time by adding a fluorescently labeled oligonucleotide complementary to oligonucleotide barcode, imaging, stripping, and repeating this cycle. With this we developed a panel of 46 antibodies that was used to stain five human lymphoid tissues: three tonsils, a spleen, and a LN. To analyze the data produced, an image processing and analysis pipeline was developed that enabled single‐cell analysis on the data, including unsupervised clustering, that revealed 31 cell types across all tissues. We compared cell‐type compositions within and directly surrounding follicles from the different lymphoid organs and evaluated cell‐cell density correlations. This sequential oligonucleotide exchange technique enables a facile imaging of tissues that leverages pre‐existing imaging infrastructure to decrease the barriers to broad use of multiplexed imaging., We simplified the multiplexed imaging method CO‐Detection by indEXing (CODEX) by employing cyclic stripping and annealing of fluorescent oligonucleotides to DNA‐barcoded antibodies. We stained five human lymphoid tissues with a panel of 46 antibodies. This enabled quantitative spatial single‐cell biology analysis of differential cell‐type composition of immune follicles.

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cell’s protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

Chemical modifications, including conjugation of antibodies to DNA tags, can lead to antibody inactivation. For that reason it is important to validate an antibody conjugate before adding to a CODEX panel.The antibody validation protocol is a series of experiments performed according to other protocols that are used to achieve a final judgement on the performance of a given antibody-oligo conjugate. An antibody conjugate should be detectable at high resolution, without need for intensity thresholding, with exposure times less than or equal to 0.5s to be determined valid for inclusion in a CODEX panel.

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell’s marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell–cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.

Selective differentiation of naive T cells into multipotent T cells is of great interest clinically for the generation of cell-based cancer immunotherapies. Cellular differentiation depends crucially on division state and time. Here we adapt a dye dilution assay for tracking cell proliferative history through mass cytometry and uncouple division, time and regulatory protein expression in single naive human T cells during their activation and expansion in a complex ex vivo milieu. Using 23 markers, we defined groups of proteins controlled predominantly by division state or time and found that undivided cells account for the majority of phenotypic diversity. We next built a map of cell state changes during naive T-cell expansion. By examining cell signaling on this map, we rationally selected ibrutinib, a BTK and ITK inhibitor, and administered it before T cell activation to direct differentiation toward a T stem cell memory (TSCM)-like phenotype. This method for tracing cell fate across division states and time can be broadly applied for directing cellular differentiation.

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers., Maeve O’Huallachain et al. report a method that enables simultaneous, ultra-high throughput single-cell barcoding for targeted single-cell protein and RNA analysis. They show the utility of their method in analyses of mRNA and protein expression in human and mouse cells.

Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level. Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis. These features implicated the pro-BII subpopulation of B cells with activated mTOR signaling, and the pre-BI subpopulation of B cells with activated and unresponsive pre-B cell receptor signaling, to be associated with relapse. This model, termed 'developmentally dependent predictor of relapse' (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. By leveraging a data-driven approach, we demonstrate the predictive value of single-cell 'omics' for patient stratification in a translational setting and provide a framework for its application to human cancer.

SUMMARY We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson’s diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring., Graphical Abstract, In Brief Although genetic and proteomic data from bulk-processed HGSOC tumors exist, critical information about rare cell subsets is lost. Using multiparametric CyTOF analysis of viable single cells from HGSOC tumors, Gonzalez et al. uncover cell types recurring across tumors with potential roles in metastasis and disease progression.

CODEX is a technology that uses oligo labeled antibodies, specialized fluorescent probes, and a companion instrument along-side a standard fluorescence microscope to create single-cell resolution fluorescence data across a multitude of parameters within spatial context in a single tissue. CODEX was developed by Yury Goltsev and Nikolay Samusik in the laboratory of Garry Nolan at Stanford. The technology is being commercialized by Akoya Biosciences.

CODEX is a technology that uses oligo labeled antibodies, specialized fluorescent probes, and a companion instrument along-side a standard fluorescence microscope to create single-cell resolution fluorescence data across a multitude of parameters within spatial context in a single tissue. CODEX was developed by Yury Goltsev and Nikolay Samusik in the laboratory of Garry Nolan at Stanford. The technology is being commercialized by Akoya Biosciences.

Growing evidence suggests that tumor progression can interfere with normal hematopoiesis and skew the host system to undergo myeloid biased changes. Hematopoietic stem and progenitor cells (HSPCs) are a rare population of precursor cells giving rise to progeny that replenish blood cells throughout life. HSPCs are mainly localized in the bone marrow niche in healthy adults, however pathophysiological conditions such as cancer can cause extramedullary hematopoiesis in organs like the spleen. In this study, we investigated tumor-driven phenotypic and molecular alterations in the HSPC compartment at a single-cell level during extramedullary hematopoiesis using single-cell multiomics. We used a B16-F10 melanoma mouse model to uncover the tumor-mediated changes. Mice with tumor-burden showed splenomegaly and alterations in the composition of the HSPC compartment in the spleen consistent with extramedullary hematopoiesis. We then isolated four HSPC populations, including Lin- Sca1+ cKit+ (LSK), common myeloid progenitor (CMP), granulocyte-macrophage progenitor (GMP) and megakaryocyte-erythrocyte progenitor (MEP) cells from the spleen and bone marrow of melanoma-burdened mice using the BD FACSMelody™ Cell Sorter. Next, we used an integrated workflow on the BD Rhapsody™ Single-Cell Analysis System for the downstream single-cell multiomics analysis. We utilized oligo-conjugated antibodies for NGS-based sample multiplexing and protein detection (BD® Single-Cell Multiplexing Kit, BD® AbSeq Assay), along with whole transcriptome analysis for a comprehensive analysis of mRNA expression. Data analysis using the X-shift algorithm paired with single-cell force-directed layout visualization enabled delineation of the developmental trajectories of HSPC populations and exploration of phenotypic and transcriptomic changes across different hematopoietic subpopulations and across distinct anatomic sites. The analysis also identified unique hematopoietic cell clusters present during melanoma-driven extramedullary hematopoiesis in the spleen. Overall, our findings highlight melanoma-driven perturbations in hematopoiesis by utilizing advanced single-cell multiomics technology. Disclaimers: For Research Use Only. Not for use in diagnostic or therapeutic procedures. Class 1 Laser Product. BD-23277 (v1.0) 1120 BD, the BD Logo, FACSMelody and Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2020 BD. All rights reserved. Citation Format: Nihan Kara, Nikolay Samusik, Xiaoshan Shi, Chip Lomas, Stephanie Widmann, Aaron J. Tyznik. Single-cell trajectory analysis reveals a melanoma-driven distinct hematopoietic response in murine spleen [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3201.

Hematopoietic stem and progenitor cells (HSPCs) are a rare population of precursor cells residing in bone marrow that replenish blood cells throughout adult life. Growing evidence suggests that tumor progression can interfere with normal hematopoiesis, skew the host system to undergo myeloid biased changes and cause extramedullary hematopoiesis (EMH) in organs such as the spleen. In this study, we investigated tumor-driven phenotypic and molecular alterations in HSPCs during EMH using single-cell multiomics. We used a B16-F10 melanoma mouse model which showed splenomegaly and alterations in the composition of the HSPCs in the spleen consistent with EMH. We then isolated four HSPC populations from the spleen and bone marrow of melanoma-burdened mice using the BD FACSMelody™ Cell Sorter and used the BD Rhapsody™ Single-Cell Analysis System for the downstream single-cell multiomics analysis. We utilized oligo-conjugated antibodies for NGS-based sample multiplexing (BD® Single-Cell Multiplexing Kit) and protein detection (BD® AbSeq Assay), along with whole transcriptome analysis for a comprehensive analysis of mRNA expression. We observed tumor-driven distinct hematopoietic response and alterations in developmental trajectories of HSPCs during EMH using the X-shift algorithm paired with single-cell force-directed layout visualization. Our findings highlight melanoma-driven perturbations in hematopoiesis by utilizing single-cell multiomics in a murine model. Class 1 Laser Product. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD-24477 (v1.0) 1220 BD, the BD Logo, FACSMelody, Rhapsody and AbSeq are trademarks of Becton, Dickinson and Company or its affiliates ©2020 BD. All rights reserved.

Cancer is associated with concomitant myeloid cell responses that are characterized by an expansion of tumor associated myeloid cells including, myeloid derived suppressor cells (MDSCs), tumor associated macrophages (TAMs) and neutrophils (TANs). These myeloid derived cells are known to modulate immune responses that enhance cancer cell stemness, angiogenesis, metastasis, invasion and immune escape. The prevailing paradigm is that cancer interferes with hematopoiesis and skews the host system to generate cells of myeloid origin with tumor-promoting functions. Intriguingly, tumor derived factors can perturbate normal bone marrow hematopoiesis and promote extramedullary hematopoiesis in organs such as the spleen. Advances in single cell multiomic technologies now enable the analysis of high dimensional protein and mRNA expression from thousands of cells simultaneously. Using the power of the BD Rhapsody™ Single-Cell Analysis system, we have carefully investigated melanoma associated extramedullary hematopoiesis in mice. To do this, we isolated four hematopoietic stem and progenitor cell populations simultaneously, including Lin- Sca-1+ c-Kit+ (LSK), common myeloid progenitor (CMP), granulocyte-macrophage progenitor (GMP) and megakaryocyte-erythrocyte progenitor (MEP) cells from the spleen and bone marrow of melanoma-bearing mice using the BD FACSMelody™ cell sorter in combination with a multiplex BD® AbSeq Oligos panel for surface protein expression and a single-cell whole transcriptome analysis. Using advanced analysis plugins in SeqGeq™ software, we were able to delineate the developmental trajectories of these hematopoietic stem and progenitor cells in this systemic immuno-suppressive myeloid environment. These advanced technologies are critical to uncover tumor mediated abnormalities in hematopoiesis. Disclaimers: For Research Use Only. Not for use in diagnostic or therapeutic procedures. Class 1 Laser Product. BD, the BD Logo, FACSMelody and Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2019 BD. All rights reserved. Citation Format: Nihan Kara, Xiaoshan Shi, Nikolay Samusik, Stephanie Widmann, Aaron J. Tyznik. A single-cell multiomics approach to study tumor-driven perturbations during hematopoiesis in mice [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4497.

Cancer is associated with concomitant myeloid cell responses that are characterized by an expansion of tumor associated myeloid cells, including myeloid derived suppressor cells, tumor associated macrophages and neutrophils. The prevailing paradigm is that cancer interferes with normal hematopoiesis by skewing the generation of myeloid cells with tumor promoting functions and promoting extramedullary hematopoiesis in organs such as the spleen. Advances in single cell multiomic technologies now enable the analysis of high dimensional protein and mRNA expression from thousands of cells simultaneously. Using the power of the BD Rhapsody™ Single-Cell Analysis System, we have carefully investigated melanoma associated extramedullary hematopoiesis in mice. To do this, we isolated 4 hematopoietic stem and progenitor cell populations simultaneously, including Lin− Sca-1+ c-Kit+, common myeloid progenitor, granulocyte-macrophage progenitor, and megakaryocyte-erythrocyte progenitor cells from the spleen and bone marrow of melanoma bearing mice using the BD FACSMelody™ cell sorter in combination with a multiplex BD® AbSeq Oligos panel for surface protein expression and a single-cell whole transcriptome analysis. Using advanced analysis plugins in SeqGeq™ software, we were able to delineate the developmental trajectories of these hematopoietic stem and progenitor cells in this systemic immuno-suppressive myeloid environment. These advanced technologies are critical to uncover tumor mediated abnormalities in hematopoiesis. Disclaimers: For Research Use Only. Class 1 Laser Product. BD, the BD Logo, FACSMelody and Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2019 BD. All rights reserved.

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately,70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

Summary Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We re-engineered co-detection by indexing (CODEX) for paraffin-embedded tissue microarrays, enabling simultaneous profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers. We identified nine conserved, distinct cellular neighborhoods (CNs)—a collection of components characteristic of the CRC iTME. Enrichment of PD-1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating how complex biological processes, such as antitumoral immunity, occur through concerted actions of cells and spatial domains., Graphical Abstract, Highlights • FFPE-CODEX multiplexed tissue imaging of 56 markers in 140 tissues of 35 CRC patients • Cellular neighborhoods reveal spatial organization of the tumor microenvironment • Altered organization of tumor and immune components in low- versus high-risk patients • Local enrichment of PD-1+CD4+ T cells correlates with survival in high-risk patients, A multiplexed tissue imaging and computational analysis framework applied to colorectal cancer allows interrogation of how spatial organization of the immune tumor microenvironment is linked to clinical outcomes.

Motivation High-parameter single-cell technologies can reveal novel cell populations of interest, but studying or validating these populations using lower-parameter methods remains challenging. Results Here, we present GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time. A series of case studies of complex cell types illustrates how simplified enrichment strategies can enable more efficient assays, reveal novel biomarkers and clarify underlying biology. Availability and implementation The GateFinder algorithm is implemented as a free and open-source package for BioConductor: https://nalab.stanford.edu/gatefinder. Supplementary information Supplementary data are available at Bioinformatics online.

Transcriptome mapping in the 3D brain RNA sequencing samples the entire transcriptome but lacks anatomical information. In situ hybridization, on the other hand, can only profile a small number of transcripts. In situ sequencing technologies address these shortcomings but face a challenge in dense, complex tissue environments. Wang et al. combined an efficient sequencing approach with hydrogel-tissue chemistry to develop a multidisciplinary technology for three-dimensional (3D) intact-tissue RNA sequencing (see the Perspective by Knöpfel). More than 1000 genes were simultaneously mapped in sections of mouse brain at single-cell resolution to define cell types and circuit states and to reveal cell organization principles. Science , this issue p. eaat5691 ; see also p. 328

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.

Acute myeloid leukemia (AML) is characterized by a high relapse rate that has been attributed to the quiescence of leukemia stem cells (LSC), which renders them resistant to chemotherapy. However, this hypothesis is largely supported by indirect evidence and fails to explain the large differences in relapse rates across AML subtypes. To address this, bone marrow aspirates from 41 AML patients and five healthy donors were analyzed by high-dimensional mass cytometry. All patients displayed immunophenotypic and intracellular signaling abnormalities within CD34+CD38lo populations, and several karyotype- and genotype-specific surface marker patterns were identified. The immunophenotypic stem and early progenitor cell populations from patients with clinically favorable core-binding factor AML demonstrated a 5-fold higher fraction of cells in S-phase compared with other AML samples. Conversely, LSCs in less clinically favorable FLT3-ITD AML exhibited dramatic reductions in S-phase fraction. Mass cytometry also allowed direct observation of the in vivo effects of cytotoxic chemotherapy. Significance: The mechanisms underlying differences in relapse rates across AML subtypes are poorly understood. This study suggests that known chemotherapy sensitivities of common AML subsets are mediated by cell-cycle differences among LSCs and provides a basis for using in vivo functional characterization of AML cells to inform therapy selection. Cancer Discov; 5(9); 988–1003. ©2015 AACR. See related commentary by Do and Byrd, p. 912. This article is highlighted in the In This Issue feature, p. 893

A cytometric imaging approach, called CO-Detection by indEXing (CODEX), that enables high parameter multiplexing of antibody-tagged target epitopes is used here to create high parameter imaging datasets of normal mouse and lupus (MRL/lpr) spleens. In this procedure, antibody binding events are rendered iteratively using DNA barcodes, fluorescent dNTP analogs, and an in-situ polymerization-based indexing procedure. Fluorescent signals from multiple rounds of indexing are computationally combined into a multi-channel image stack and subjected to image segmentation and quantification. A segmentation and linear model algorithm was developed to accurately quantify membrane antigen levels on dissociated cells as well as tissue sections. Leveraging the spatially resolved nature of CODEX multiplexed single-cell imaging data, quantitative de novo characterization of lymphoid tissue architecture was enabled and overlaid onto previously described morphological features. 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 imaging data analysis demonstrated here will allow deep proteomic analysis and systematic characterization of complex tissue architecture in normal and clinically aberrant samples.

Neuroinflammation and neurodegeneration may represent two polarities of brain pathology. Brain myeloid cells, particularly microglia, play key roles in these conditions. Here, we employed single-cell mass cytometry (CyToF) to compare myeloid cell populations in the experimental autoimmune encephalomyelitis (EAE) model of neuroinflammation, the R6/2 model of Huntington’s disease (HD), and the superoxide dismutase 1 (mSOD1) model of amyotrophic lateral sclerosis (ALS). We identified three myeloid cell populations exclusive to the central nervous system (CNS) and present in each disease model. Blood-derived monocytes comprised five populations and migrated to the brain in EAE, but not in HD and ALS models. Single-cell analysis resolved differences in signaling and cytokine production within similar myeloid populations in EAE compared to HD and ALS models. Moreover, these analyses highlighted α5 integrin on myeloid cells as a potential therapeutic target for neuroinflammation. Together, these findings illustrate how neuropathology may differ between inflammatory and degenerative brain disease.

Summary 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., Graphical Abstract, Highlights • Autoimmunity analyzed by multiplexed DNA-tagged antibody staining (CODEX) • CODEX data reveal pairwise interactions and niches changing with disease • First tier of neighbors significantly impacts marker expression in the index cells • Changes in splenic morphology correlate with shifts in cell frequencies, A DNA barcoding-based imaging technique uses multiplexed tissue antigen staining to enable the characterization of cell types and dynamics in a model of autoimmune disease.

Muscle regeneration is a dynamic process during which cell state and identity change over time. A major roadblock has been a lack of tools to resolve a myogenic progression in vivo. Here we capitalize on a transformative technology, single-cell mass cytometry (CyTOF), to identify in vivo skeletal muscle stem cell and previously unrecognized progenitor populations that precede differentiation. We discovered two cell surface markers, CD9 and CD104, whose combined expression enabled in vivo identification and prospective isolation of stem and progenitor cells. Data analysis using the X-shift algorithm paired with single-cell force directed layout visualization, defined a molecular signature of the activated stem cell state (CD44+/CD98+/MyoD+) and delineated a myogenic trajectory during recovery from acute muscle injury. Our studies uncover the dynamics of skeletal muscle regeneration in vivo and pave the way for the elucidation of the regulatory networks that underlie cell-state transitions in muscle diseases and aging.

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject., Author summary Recently, the cytometry field has experienced rapid advancement in the development of mass cytometry (CyTOF). CyTOF enables a significant increase in the ability to monitor 50 or more cellular markers for millions of cells at the single-cell level. Initial studies with CyTOF focused on few samples, in which expert manual discovery of cell types were acceptable. As the technology matures, it is now feasible to collect more samples, which enables systematic studies of cell types across multiple samples. However, the statistical and computational issues surrounding multi-sample analysis have not been previously examined in detail. Furthermore, it was not clear how the data analysis could be scaled for hundreds of samples, such as those in clinical studies. In this work, we present a scalable analysis pipeline that is grounded in strong statistical foundation. Partition-Assisted Clustering (PAC) offers fast and accurate clustering and Multiple Alignments of Networks (MAN) utilizes network structures learned from each homogeneous cluster to organize the data into data-set level clusters. PAC-MAN thus enables the analysis of a large CyTOF dataset that was previously too large to be analyzed systematically; this pipeline can be extended to the analysis of similarly large or larger datasets.

Recent technological developments allow gathering single-cell measurements across different domains (genomic, transcriptomics, proteomics, imaging etc). Sophisticated computational algorithms are required in order to harness the power of single-cell data. This session is dedicated to computational methods for single-cell analysis in various biological domains, modelling of population heterogeneity, as well as translational applications of single cell data.

Introduction: Endogenous methylarginines have been proposed as markers and potentially mediators of cardiovascular diseases. Alanine:glyoxylate aminotransferase 2 (AGXT2) is the only enzyme capable of regulation of plasma levels of all three endogenous methylarginines. It has also been demonstrated that AGXT2 and its alternative substrate beta-aminoisobutyric acid (BAIB) can play an important modulatory role in lipid metabolism. Using bioinformatic analysis we identified a highly conserved putative binding site for the diabetes-associated transcription factor hepatic nuclear factor 4 alpha (HNF4A) in the mammalian AGXT2 promoter region. The aim of this study was to test the hypothesis that HNF4a is the major regulator of AGXT2 expression and activity. Methods and results: We introduced several point mutations in the putative HNF4A binding site and investigated their influence on activity of the murine Agxt2 promoter using luciferase reporter assay. The mutated constructs decreased the activity of the reporter gene by 75% as compared to the native promoter sequence. We showed direct binding of HNF4a to Agxt2 promoter using chromatin immunoprecipitation. We were able to demonstrate that siRNA-mediated knockdown of HNF4a leads to 50% reduction of Agxt2 expression in the murine hepatic cell line Hepa 1-6. In the in-vivo part of the project we showed that liver-specific Hnf4a knockout mice have a 90% reduction in liver Agxt2 mRNA levels, a 85% decrease in liver AGXT2 activity and significantly increased plasma levels of endogenous methylarginines and BAIB. Conclusions: In the current study we showed direct binding of HNF4a to the mammalian AGXT2 promoter region. We also demonstrated using in-vitro and in-vivo approaches that HNF4A is the major regulator of Agxt2 expression and has direct influence on systemic levels of endogenous methylarginines and BAIB. These findings suggest a novel link between NO-mediated impairment of vascular and renal function and lipid metabolism.

The expansion of the neocortex during mammalian brain evolution results primarily from an increase in neural progenitor cell divisions in its two principal germinal zones during development, the ventricular zone (VZ) and the subventricular zone (SVZ). Using mRNA sequencing, we analyzed the transcriptomes of fetal human and embryonic mouse VZ, SVZ, and cortical plate. In mouse, the transcriptome of the SVZ was more similar to that of the cortical plate than that of the VZ, whereas in human the opposite was the case, with the inner and outer SVZ being highly related to each other despite their cytoarchitectonic differences. We describe sets of genes that are up- or down-regulated in each germinal zone. These data suggest that cell adhesion and cell–extracellular matrix interactions promote the proliferation and self-renewal of neural progenitors in the developing human neocortex. Notably, relevant extracellular matrix-associated genes include distinct sets of collagens, laminins, proteoglycans, and integrins, along with specific sets of growth factors and morphogens. Our data establish a basis for identifying novel cell-type markers and open up avenues to unravel the molecular basis of neocortex expansion during evolution.

Introduction The bone marrow (BM) microenvironment consists of various cell types such as mesenchymal stromal cells, endothelial cells, osteoblastic cells and multiple immune cell types including mature myeloid cells and lymphocytes. Recent studies have shown that leukemias can create and maintain a leukemia-supporting BM microenvironment, and vice versa, a dysfunctional BM microenvironment can contribute to leukemia development and progression. Moreover, in tumors the microenvironment is often immunosuppressive and restrains effective anti-tumoral immune responses by adaptive and innate immunity. A better understanding of the precise localization of microenvironmental and immune cell types in intact tissue, and how they physically interact with each other and with tumor cells, will improve our understanding of the mechanisms by which cancer reprograms its microenvironment and may form the basis for novel immunotherapies. Methods CO-Detection by antibody indEXing (CODEX) is a multiplex fluorescence microscopy platform based on DNA-conjugated antibodies that allows the analysis of 50+ markers in a single tissue section. After staining with an antibody cocktail, tissues are imaged in a multi-cycle reaction using a microfluidics system and a fluorescence microscope with a computer automated X/Y/Z stage. DNA-conjugated antibodies are rendered visible using complementary fluorescent DNA probes, followed by imaging, probe stripping, washing and re-rendering. This process is repeated until all the antibodies present in the initial cocktail have been rendered and imaged. Here, we used CODEX to analyze intact BM at the single-cell level (~200nm resolution) during leukemic progression. Chronic myeloid leukemia (CML)-like disease was induced in non-irradiated mice using BCR-ABL1-GFP retrovirus. Tissue sections of femoral bones harvested at different time points after leukemia onset were stained using a 50+ marker CODEX antibody panel to simultaneously identify hematopoietic and leukemic stem and progenitor cells, multiple BM microenvironmental cell types, myeloid and lymphoid cells as well as functional markers. Results We have built an integrated computational pipeline for the analysis of high-dimensional CODEX data that enables the identification and characterization of BM cell types as well as their spatial organization in situ. Raw images were concatenated and aligned using Hoechst nuclear stain as a reference, followed by deconvolution, segmentation, marker expression quantification and spatial compensation. Exported data were clustered in an unsupervised manner using VorteX algorithm, which identified 28 distinct cellular clusters based on marker expression values. All major BM compartments including stromal (vascular, pericytes, osteoblastic), lymphoid (T and B cell subsets), myeloid (megakaryocytes, macrophages, dendritic cells, granulocytes) and progenitor cell types, as well as leukemic cells, were represented. During leukemic progression, the BM microenvironment was dramatically rearranged. Besides the expected growth of the leukemic clone, we observed a massive increase in vascular and osteoblastic cell types, whereas immune cell clusters were significantly reduced. Interestingly, CD71, the transferrin receptor, was strongly up-regulated on tumor cells in advanced leukemia, indicating towards a role for iron metabolism in malignant progression. Furthermore, hierarchical clustering of tissue regions based on cellular composition using X/Y/Z positional information pointed towards the emergence of specific cell-cell interaction modules that developed during leukemic progression, including mutual attraction between B cells and central arterioles. Conclusions High-dimensional imaging of the BM microenvironment by CODEX allows studying the abundance and distribution of cellular elements that are often underestimated or missed by traditional flow cytometry, such as stromal cells, vasculature and megakaryocytes. Importantly, CODEX identifies single cells in their tissue context during leukemic progression and facilitates the discovery of novel cell-cell interactions and cell types as well as unexpected marker constellations. Disclosures Samusik: Akoya Biosciences: Consultancy, Equity Ownership, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Nolan:Akoya Biosciences: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Goltsev:Akoya Biosciences: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.

Earlier we have identified a new class of human genomic sequences expressed preferentially in tumors. Here we use a comparative genomics approach and conservation analysis to study evolutionary specificity of nine human tumor-specific sequences, described previously. Three sequences had originated in the primate lineage. The other three had mammalian orthologs, but conservation analysis has shown that these sequences evolved neutrally. Three sequences were conservative. These data confirm previously formulated hypothesis that evolutionarily new genes are expressed in tumors.

Accurate and rapid identification of cell populations is key to discovering novelty in multidimensional single cell experiments. We present a population finding algorithm X-shift that can process large datasets using fast KNN estimation of cell event density and automatically arranges populations by a marker-based classification system. X-shift analysis of mouse bone marrow data resolved the majority of known and several previously undescribed cell populations. Interestingly, previously known cell populations, as well as intermediate cell populations in early hematopoietic development, were described via novel marker combinations that were defined via routes to their locations in expressed marker space. X-shift provides a rapid, reliable approach to managed cell subset analysis that maximizes automation that not only best mimics human intuition, but as we show provides access to novel insights that “prior knowledge” might prevent the researcher from visualizing.

Introduction: Alanine-glyoxylate aminotransferase 2 (AGXT2) is the only known enzyme capable of degradation of all three endogenous methylarginines, which serve as markers and potentially mediators of cardiovascular disease. Recent studies also suggest that AGXT2 and its alternative substrate beta-aminoisobutyric acid (BAIB) play important role in lipid metabolism. The predicted core promoter region of mammalian AGXT2 promoter contains a highly conserved putative binding site for hepatic nuclear factor 4 alpha (HNF4A). Patients with severe deficiency in HNF4a develop maturity onset diabetes of young 1. Furthermore, polymorphisms of HNF4A are associated with increased risk of diabetes type 2. The aim of this study was to test the hypothesis that HNF4A is a major regulator of AGXT2 expression and activity. Methods and results: We demonstrated direct binding of HNF4A to the Agxt2 promoter region in hepatic cell line Hepa 1-6 using chromatin immunoprecipitation assays. Then we showed that mutations of the predicted HNF4A binding site in the Agxt2 core promoter result in up to 80% decrease in the promoter activity as assessed by luciferase reporter assays (p Conclusions: In our study we identified HNF4A as the major regulator of Agxt2 gene expression. This finding suggests that diabetic patients with HNF4A deficiency might have a unique mechanism for development of cardiovascular complication via AGXT2-dependent impairment of lipid metabolism and methylarginines-mediated vascular dysfunction.

In the version of this Article originally published, the name of author Andrew Tri Van Ho was coded wrongly, resulting in it being incorrect when exported to citation databases. This has been corrected, though no visible changes will be apparent.

The tumor microenvironment plays a critical role in cancer progression and has implications for the efficacy of various cancer immunotherapy treatment options. Immune infiltrates within the tumor microenvironment can correlate with both positive and negative outcomes, depending upon the both the type of cancer as well as infiltrating immune cell(s). These analyses are typically performed using standard immunofluorescence and immunohistochemistry assays where no more than four simultaneous parameters can be visualized on the same tissue. Unfortunately, these tools cannot fully characterize the complexity of the tumor microenvironment due to the inherent limitations of fluorophore spectral overlap. In order to identify each type of immune and tumor cell within a single tissue, at least 40 parameters need to be measured simultaneously. We have developed a multiparametric immunofluorescence technology, entitled CODEX (Co-Detection by IndEXing), which utilizes unique DNA tags as a means of iteratively measuring more than 40 parameters within the same tissue. More than 40 human antibodies have been validated using this approach, including numerous immune markers, checkpoint ligands, tumor markers and cellular activity markers. We are currently analyzing tissue sample from patients with lung cancer. By measuring nearly 50 simultaneous markers within the same tissue, CODEX has the potential to greatly enhance our knowledge of the tumor microenvironment and more accurately define immune infiltrates at the single-cell level. Citation Format: Julia Kennedy-Darling, Garry P. Nolan, Yury Goltsev, Nikolay Samusik. Multiparametric immunofluorescence analysis of the tumor microenvironment using CODEX [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr A089.

Endocytosis is a complex process fulfilling many cellular and developmental functions. Understanding how it is regulated and integrated with other cellular processes requires a comprehensive analysis of its molecular constituents and general design principles. Here, we developed a new strategy to phenotypically profile the human genome with respect to transferrin (TF) and epidermal growth factor (EGF) endocytosis by combining RNA interference, automated high-resolution confocal microscopy, quantitative multiparametric image analysis and high-performance computing. We identified several novel components of endocytic trafficking, including genes implicated in human diseases. We found that signalling pathways such as Wnt, integrin/cell adhesion, transforming growth factor (TGF)-beta and Notch regulate the endocytic system, and identified new genes involved in cargo sorting to a subset of signalling endosomes. A systems analysis by Bayesian networks further showed that the number, size, concentration of cargo and intracellular position of endosomes are not determined randomly but are subject to specific regulation, thus uncovering novel properties of the endocytic system.