Development of a high-throughput image cytometric screening method as a research tool for immunophenotypic characterization of patient samples from clinical studies.

Immunophenotyping has been the primary assay for characterization of immune cells from patients undergoing therapeutic treatments in clinical research, which is critical for understanding disease progression and treatment efficacy. Currently, flow cytometry has been the dominant methodology for characterizing surface marker expression for immunological research. Flow cytometry has been proven to be an effective and efficient method for immunophenotyping, however, it requires highly trained users and a large time commitment. Recently, a novel image cytometry system (Cellaca® PLX Image Cytometer, Revvity Health Sciences, Inc., Lawrence, MA) has been developed as a complementary method to flow cytometry for performing rapid and high-throughput immunophenotyping. In this work, we demonstrated an image cytometric screening method to characterize immune cell populations, streamlining the analysis of routine surface marker panels. The T cell, B cell, NK cell, and monocyte populations of 46 primary PBMC samples from subjects enrolled in autoimmune and oncological disease study cohorts were analyzed with two optimized immunophenotyping staining kits: Panel 1 (CD3, CD56, CD14) and Panel 2 (CD3, CD56, CD19). We validated the proposed image cytometry method by comparing the Cellaca® PLX and the AuroraTM flow cytometer (Cytek Biosciences, Fremont, CA). The image cytometry system was employed to generate bright field and fluorescent images, as well as scatter plots for multiple patient PBMC samples. In addition, the image cytometry method can directly determine cell concentrations for downstream assays. The results demonstrated comparable CD3, CD14, CD19, and CD56 cell populations from the primary PBMC samples, which showed an average of 5% differences between flow and image cytometry. The proposed image cytometry method provides a novel research tool to potentially streamline immunophenotyping workflow for characterizing patient samples in clinical studies.

Trimodal single-cell profiling reveals a novel pediatric CD8αα+ T cell subset and broad age-related molecular reprogramming across the T cell compartment.

Age-associated changes in the T cell compartment are well described. However, limitations of current single-modal or bimodal single-cell assays, including flow cytometry, RNA-seq (RNA sequencing) and CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing), have restricted our ability to deconvolve more complex cellular and molecular changes. Here, we profile >300,000 single T cells from healthy children (aged 11-13 years) and older adults (aged 55-65 years) by using the trimodal assay TEA-seq (single-cell analysis of mRNA transcripts, surface protein epitopes and chromatin accessibility), which revealed that molecular programming of T cell subsets shifts toward a more activated basal state with age. Naive CD4+ T cells, considered relatively resistant to aging, exhibited pronounced transcriptional and epigenetic reprogramming. Moreover, we discovered a novel CD8αα+ T cell subset lost with age that is epigenetically poised for rapid effector responses and has distinct inhibitory, costimulatory and tissue-homing properties. Together, these data reveal new insights into age-associated changes in the T cell compartment that may contribute to differential immune responses.

A comprehensive platform for analyzing longitudinal multi-omics data.

Longitudinal bulk and single-cell omics data is increasingly generated for biological and clinical research but is challenging to analyze due to its many intrinsic types of variations. We present PALMO ( https://github.com/aifimmunology/PALMO ), a platform that contains five analytical modules to examine longitudinal bulk and single-cell multi-omics data from multiple perspectives, including decomposition of sources of variations within the data, collection of stable or variable features across timepoints and participants, identification of up- or down-regulated markers across timepoints of individual participants, and investigation on samples of same participants for possible outlier events. We have tested PALMO performance on a complex longitudinal multi-omics dataset of five data modalities on the same samples and six external datasets of diverse background. Both PALMO and our longitudinal multi-omics dataset can be valuable resources to the scientific community.

Cross-platform immunophenotyping of human peripheral blood mononuclear cells with four high-dimensional flow cytometry panels.

Immunophenotyping using high dimensional flow cytometry is a central component of human immune system multi-omic studies. We present four high parameter flow cytometry panels for deep immunophenotyping of human peripheral blood mononuclear cells (PBMC). This set of four 25+ color panels include 64 cell surface markers to resolve broad immune compartment populations, as well as activation and memory of specific T, B, natural killer (NK), and myeloid lineages. Common lineage bridging markers are integrated into each panel to allow for inter-panel quality control through major lineage frequency verification. These panels were developed using a five laser BD Symphony A5 conventional cytometer and successfully transferred to a five laser Cytek Aurora spectral cytometer capable of acquiring the panels. Nine representative PBMC samples were stained with the four phenotyping panels and acquired on both instruments to evaluate population frequency and visual staining patterns for gating between the systems. Both instruments produced comparable high quality flow cytometry data and supported our decision to acquire samples on the spectral cytometer moving forward. This modular set of panels and instrument performance metrics provide guidelines for designing flow cytometry experiments suitable for longitudinal or cross-sectional immune profiling.

BarWare: efficient software tools for barcoded single-cell genomics.

BACKGROUND: Barcode-based multiplexing methods can be used to increase throughput and reduce batch effects in large single-cell genomics studies. Despite advantages in flexibility of sample collection and scale, there are additional complications in the data deconvolution steps required to assign each cell to their originating samples. RESULTS: To meet computational needs for efficient sample deconvolution, we developed the tools BarCounter and BarMixer that compute barcode counts and deconvolute mixed single-cell data into sample-specific files, respectively. Together, these tools are implemented as the BarWare pipeline to support demultiplexing from large sequencing projects with many wells of hashed 10x Genomics scRNA-seq data. CONCLUSIONS: BarWare is a modular set of tools linked by shell scripting: BarCounter, a computationally efficient barcode sequence quantification tool implemented in C; and BarMixer, an R package for identification of barcoded populations, merging barcoded data from multiple wells, and quality-control reporting related to scRNA-seq data. These tools and a self-contained implementation of the pipeline are freely available for non-commercial use at https://github.com/AllenInstitute/BarWare-pipeline .

Optimized workflow for human PBMC multiomic immunosurveillance studies.

Deep immune profiling is essential for understanding the human immune system in health and disease. Successful biological interpretation of this data requires consistent laboratory processing with minimal batch-to-batch variation. Here, we detail a robust pipeline for the profiling of human peripheral blood mononuclear cells by both high-dimensional flow cytometry and single-cell RNA-seq. These protocols reduce batch effects, generate reproducible data, and increase throughput. For complete details on the use and execution of this protocol, please refer to Savage et al. (2021).

Longitudinal immune dynamics of mild COVID-19 define signatures of recovery and persistence.

SARS-CoV-2 has infected over 200 million and caused more than 4 million deaths to date. Most individuals (>80%) have mild symptoms and recover in the outpatient setting, but detailed studies of immune responses have focused primarily on moderate to severe COVID-19. We deeply profiled the longitudinal immune response in individuals with mild COVID-19 beginning with early time points post-infection (1-15 days) and proceeding through convalescence to >100 days after symptom onset. We correlated data from single cell analyses of peripheral blood cells, serum proteomics, virus-specific cellular and humoral immune responses, and clinical metadata. Acute infection was characterized by vigorous coordinated innate and adaptive immune activation that differed in character by age (young vs. old). We then characterized signals associated with recovery and convalescence to define and validate a new signature of inflammatory cytokines, gene expression, and chromatin accessibility that persists in individuals with post-acute sequelae of SARS-CoV-2 infection (PASC).

Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq.

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.