Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells.

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

Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front.

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

GateFinder: projection-based gating strategy optimization for flow and mass cytometry

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

Continuous visualization of differences between biological conditions in single-cell data

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.</jats:p

Abstract PR14: Automatic identification of cell niches and immune interactions important for clinical outcomes using multiparameter imaging and deep neural networks

Abstract Multiparameter cytometry, for example with CyTOF, has enabled the interrogation of immune phenotypes in unprecedented detail in many clinical contexts. But cytometry is incapable of answering a question of critical importance to many tissue context studies, and especially understanding how local interactions between tumor cells and immune cells correlate to clinical outcomes. This becomes especially relevant to understanding the subtleties of how different immunotherapeutic approaches operate in vivo. We recently developed a multiparameter immunofluorescence technique, termed CODEX, which allows the capture of spatial information for protein and RNA expression in tissue sections. This spatial information enables us to establish not only cell-types according to traditional phenotypic surface marker expression, but also to potentially surmise specific tissue states driving clinical responses. To make sense of the high-dimensional data afforded by CODEX, we apply here state-of-the-art deep neural networks (DNNs). These networks, which have achieved superhuman classification accuracy in many diverse domains, automatically identify cells, cell niches and regions (at multiple scales) that are capable of distinguishing healthy and diseased samples. This is done in an unbiased way, with only ‘healthy’ vs. ‘disease’ labels as additional input alongside the imaging data. We first train DNNs to successfully classify multiparameter tissue images from independent replicates across conditions. Having achieved a high accuracy of classification, we set the network output to highlight cells and regions deemed to be most relevant to classify each condition. Applying this methodology to healthy and mrl (lupus) spleens stained for 30 markers, our neural network is able to successfully identify a not previously observed enrichment of cell confluences (niches) consisting of CD8 T-cells and conventional dendritic cells enriched in MRL samples, as well as other novel niches completely unpredicted by prior knowledge. Our DNN enables the systematic and unbiased discovery of specific immune interactions in any tissue type. Applying our technique to the analysis of samples from immunotherapy recipients could enable the discovery of key factors in the tumor microenvironment that distinguish positive responders as well as the subsequent identification of targets for perturbation. Citation Format: Salil S. Bhate, Nikolay Samusik, Yury Goltsev, Garry P. Nolan. Automatic identification of cell niches and immune interactions important for clinical outcomes using multiparameter imaging and deep neural networks [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 PR14.</jats:p

Abstract A089: Multiparametric immunofluorescence analysis of the tumor microenvironment using CODEX

Abstract 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.</jats:p

A single-cell multiomics approach to study tumor-driven perturbations during hematopoiesis in mice

Abstract 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.</jats:p