Single-cell transcriptomic analysis of mIHC images via antigen mapping

Govek, Kiya W.; Troisi, Emma C.; Miao, Zhen; Aubin, Rachael G.; Woodhouse, Steven; Cámara, Pablo G.

doi:10.1126/sciadv.abc5464

Cited by 29 publications

(21 citation statements)

References 68 publications

(83 reference statements)

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“…To normalize ADT libraries, we fit the distribution of ADT counts for each antibody with a two-component negative binomial mixture model, as described previously. 26 …”

Section: Methodsmentioning

confidence: 99%

Single-cell multiomics dissection of basal and antigen-specific activation states of CD19-targeted CAR T cells

Bai

Lundh

Kim

et al. 2021

J Immunother Cancer

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BackgroundAutologous T cells engineered to express a chimeric antigen receptor (CAR) specific for CD19 molecule have transformed the therapeutic landscape in patients with highly refractory leukemia and lymphoma, and the use of donor-generated allogeneic CAR T is paving the way for further breakthroughs in the treatment of cancer. However, it remains unknown how the intrinsic heterogeneities of these engineered cells mediate therapeutic efficacy and whether allogeneic products match the effectiveness of autologous therapies.MethodsUsing single-cell mRNA sequencing in conjunction with CITE-seq, we performed multiomics characterization of CAR T cells generated from healthy donor and patients with acute lymphoblastic leukemia. CAR T cells used in this study were manufactured at the University of Pennsylvania through lentiviral transduction with a CD19-4-1BB-CD3ζ construct. Besides the baseline condition, we engineered NIH-3T3 cells with human CD19 or mesothelin expression to conduct ex vivo antigen-specific or non-antigen stimulation of CAR T cells through 6-hour coculture at a 1:1 ratio.ResultsWe delineated the global cellular and molecular CAR T landscape and identified that transcriptional CAR tonic signaling was regulated by a mixture of early activation, exhaustion signatures, and cytotoxic activities. On CD19 stimulation, we illuminated the disparities of CAR T cells derived from different origins and found that donor CAR T had more pronounced activation level in correlation with the upregulation of major histocompatibility complex class II genes compared with patient CAR T cells. This finding was independently validated in additional datasets from literature. Furthermore, GM-CSF(CSF2) expression was found to be associated with functional gene productions, but it induced little impact on the CAR T activation.ConclusionsThrough integrated multiomics profiling and unbiased canonical pathway analyses, our results unveil heterogeneities in the transcriptional, phenotypic, functional, and metabolic profiles of donor and patient CAR T cells, providing mechanistic basis for ameliorating clinical outcomes and developing next-generation ‘off- the-shelf’ allogeneic products.

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“…To normalize ADT libraries, we fit the distribution of ADT counts for each antibody with a two-component negative binomial mixture model, as described previously. 26 …”

Section: Methodsmentioning

confidence: 99%

Single-cell multiomics dissection of basal and antigen-specific activation states of CD19-targeted CAR T cells

Bai

Lundh

Kim

et al. 2021

J Immunother Cancer

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“…The new digital image technologies and pipelines for multiplexed immunohistochemistry (mIHC) such as CO-Detection by indexing (CODEX) can quantify the antigens at the single-cell level to characterize tissue spatial architecture ( Goltsev et al, 2018 ). A very recent new analysis method, spatially-resolved transcriptomics via epitope anchoring (STvEA), can integrate the CITE-seq data with mIHC images to achieve high-resolution of annotation for cell populations in the mIHC data to uncover the spatial transcription patterns ( Govek et al, 2021 ). STvEA integrated CITE-seq and CODEX information to identify the LR pairs, thus the results are reliable and accurate.…”

Section: Resultsmentioning

confidence: 99%

“…More specifically, we focused on three spatially colocalized cell populations including monocyte-derived macrophages, red-pulp macrophages, and neutrophils. We followed the procedures and LR annotations described in Govek et al (2021) . First, the mouse gene symbols were converted to the human ortholog symbols using the Bioconductor package biomaRT .…”

Section: Resultsmentioning

confidence: 99%

“…Two scRNA-seq datasets were used to illustrate the LR hunting approach. The first dataset is a high-quality cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) of murine spleen containing 7,097 cells with more than 1,200 mRNA unique molecular identifiers (UMIs) ( Govek et al, 2021 ). Another dataset is scRNA-seq data from five primary triple-negative breast cancer (TNBC) including 24,271 cells and 6,125 UMI detected per cell ( Wu et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

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LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data

et al. 2021

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Cell–cell interactions (CCIs) and cell–cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.

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“…Another option would be to apply a different cell phenotyping algorithm to improve cell phenotype assignments, such as a supervised approach in which the highest intensity from a group of known false positives is used as the threshold for a given marker [ 66 ]. Finally, newer approaches have been developed to assist cell annotations, such as the CITE-Seq atlases [ 67 ].…”

Section: Data Preprocessing and Quality Control Of Mif Datamentioning

confidence: 99%

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data

Wilson

Ospina

Townsend

et al. 2021

Cancers

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Immune modulation is considered a hallmark of cancer initiation and progression. The recent development of immunotherapies has ushered in a new era of cancer treatment. These therapeutics have led to revolutionary breakthroughs; however, the efficacy of immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which cancer patients will benefit from immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or “zero-inflated” data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.

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Single-cell transcriptomic analysis of mIHC images via antigen mapping

Cited by 29 publications

References 68 publications

Single-cell multiomics dissection of basal and antigen-specific activation states of CD19-targeted CAR T cells

Single-cell multiomics dissection of basal and antigen-specific activation states of CD19-targeted CAR T cells

LR Hunting: A Random Forest Based Cell–Cell Interaction Discovery Method for Single-Cell Gene Expression Data

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data

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