Psoriatic arthritis (PsA) is an immune-mediated joint disease, often developing within 10 years of onset of psoriasis, with ∼30% of patients progressing to joint involvement. Biomarkers of disease progression are essential for improving patient outcomes, however the immunological progression of disease is not fully understood. To explore the immune landscape of PsA, we isolated immune cells from 4 matched PsA blood and synovial fluid patient samples and performed multimodal single-cell sequencing. Our analysis revealed an enrichment of CD1c+ and CD141+ dendritic cells (DCs) expressing CXCL16 in the synovial fluid. Pathway analysis of these DCs indicated enhanced antigen presentation. Further analysis of blood and synovial fluid by ELISA showed high CXCL10 and CXCL16 in PsA synovial fluid. Differential gene expression of CD4 and CD8 memory T cells revealed increased granzyme K (GZMK) and CXCR3 expression in the synovial fluid T cells compared to blood. Pathway analysis of these GZMK+ T cells showed upregulation of cytotoxic pathways. Further sub-clustering of CD4 and CD8 memory populations revealed unique TCR clonal expansion associated with GZMK. Flow cytometry showed increased CXCR6+ and CXCR3+ T cells in PsA synovial fluid, implicating the CXCR6-CXCL16 and CXCR3-CXCL10 axis in immune cell migration. Together this data suggests that GZMK+ T cells may contribute to inflammation in the joint of PsA patients, after recruitment to the site of inflammation by CD1c+ & CD141+ DCs. NIH T32 National Psoriasis Foundation Immune Mechanisms of Human Disease (HUM)

Recent Hi-C technology enables more comprehensive chromosomal conformation research, including the detection of structural variations, especially translocations. In this paper, we formulate the interchromosomal translocation detection as a problem of scan clustering in a spatial point process. We then develop TranScan, a new translocation detection method through scan statistics with the control of false discovery. The simulation shows that TranScan is more powerful than an existing sophisticated scan clustering method, especially under strong signal situations. Evaluation of TranScan against current translocation detection methods on realistic breakpoint simulations generated from real data suggests better discriminative power under the receiver-operating characteristic curve. Power analysis also highlights TranScan's consistent outperformance when sequencing depth and heterozygosity rate is varied. Comparatively, Type I error rate is lowest when evaluated using a karyotypically normal cell line. Both the simulation and real data analysis indicate that TranScan has great potentials in interchromosomal translocation detection using Hi-C data.

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous group of mature T-cell neoplasms characterized by the accumulation of clonal malignant CD4+ T cells in the skin. The most common variant of CTCL, mycosis fungoides (MF ), is confined to the skin in early stages but can be accompanied by extracutaneous dissemination of malignant T cells to the blood and lymph nodes in advanced stages of disease. Sézary syndrome (SS), a leukemic form of disease, is characterized by significant blood involvement. Little is known about the transcriptional and genomic relationship between skin- and blood-residing malignant T cells in CTCL. To identify and interrogate malignant clones in matched skin and blood from patients with leukemic MF and SS, we combine T-cell receptor clonotyping with quantification of gene expression and cell surface markers at the single cell level. Our data reveal clonal evolution at a transcriptional and genetic level within the malignant populations of individual patients. We highlight highly consistent transcriptional signatures delineating skin- and blood-derived malignant T cells. Analysis of these 2 populations suggests that environmental cues, along with genetic aberrations, contribute to transcriptional profiles of malignant T cells. Our findings indicate that the skin microenvironment in CTCL promotes a transcriptional response supporting rapid malignant expansion, as opposed to the quiescent state observed in the blood, potentially influencing efficacy of therapies. These results provide insight into tissue-specific characteristics of cancerous cells and underscore the need to address the patients' individual malignant profiles at the time of therapy to eliminate all subclones.

Simultaneous measurement of surface proteins and gene expression within single cells using oligo-conjugated antibodies offers high-resolution snapshots of complex cell populations. Signal from oligo-conjugated antibodies is quantified by high-throughput sequencing and is highly scalable and sensitive. We investigated the response of oligo-conjugated antibodies towards four variables: concentration, staining volume, cell number at staining, and tissue. We find that staining with recommended antibody concentrations causes unnecessarily high background and amount of antibody used can be drastically reduced without loss of biological information. Reducing staining volume only affects antibodies targeting abundant epitopes used at low concentrations and is counteracted by reducing cell numbers. Adjusting concentrations increases signal, lowers background, and reduces costs. Background signal can account for a major fraction of total sequencing and is primarily derived from antibodies used at high concentrations. This study provides new insight into titration response and background of oligo-conjugated antibodies and offers concrete guidelines to improve such panels.

We read with interest the Letter to the Editor by Gagat et al.(2019) on the ambiguous role of special AT-rich sequence-binding protein-1 (SATB1) expression in malignant tumors. The authors make a strong point that miR-155/SATB1 axis contributes to apoptosis resistance not only in malignant T cells but is also critical in regulating cell death in a wide range of cell lines from hematologic malignancies to solid tumors. Among other findings, the authors reported that low SATB1 expression is associated with resistance to apoptosis and poor responses to UV treatment in mycosis fungoides (MF).

Multimodal single-cell assays provide high-resolution snapshots of complex cell populations, but are mostly limited to transcriptome plus an additional modality. Here, we describe expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell. We demonstrate application of ECCITE-seq to multimodal CRISPR screens with robust direct single-guide RNA capture and to clonotype-aware multimodal phenotyping of cancer samples.

Single cell RNA-sequencing (scRNA-seq) precisely characterizes gene expression levels and dissects variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for batch correction (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden factors even when they are correlated with other sources of variation by identifying a set of genes associated with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.

Rapid technological progress in the recent years has allowed the high-throughput interrogation of different types of biomolecules from single cells. Combining several of these readouts into integrated multi-omic assays is essential to comprehensively understand and model cellular processes. Here, we report the development of Expanded CRISPR-compatible Cellular Indexing of Transcriptomes and Epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell: transcriptome, immune receptor clonotypes, surface markers, sample identity and sgRNAs. We demonstrate the use of ECCITE-seq to directly and efficiently capture sgRNA molecules and measure their effects on gene expression and protein levels, opening the possibility of performing high throughput single cell CRISPR screens with multimodal readout using existing libraries and commonly used vectors. Finally, by utilizing the combined phenotyping of clonotype and cell surface markers in immune cells, we apply ECCITE to study a lymphoma sample to discriminate cells and define molecular signatures of malignant cells within a heterogeneous population.

Single cell RNA-sequencing (scRNA-seq) precisely characterize gene expression levels and dissect variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for bulk measurements (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden and correlated sources of variation by identifying a set of genes affected with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.