Cell Ranger Research Articles

RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads

BackgroundObtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based protocols the number of studied cells continues to increase rapidly. This establishes the need for software tools for efficient processing of the produced large-scale datasets. We address this need by presenting RainDrop for fast gene-cell count matrix computation from single-cell RNA-seq data produced by 10x Genomics Chromium technology.ResultsRainDrop can process single-cell transcriptomic datasets consisting of 784 million reads sequenced from around 8.000 cells in less than 40 minutes on a standard workstation. It significantly outperforms the established Cell Ranger pipeline and the recently introduced Alevin tool in terms of runtime by a maximal (average) speedup of 30.4 (22.6) and 3.5 (2.4), respectively, while keeping high agreements of the generated results.ConclusionsRainDrop is a software tool for highly efficient processing of large-scale droplet-based single-cell RNA-seq datasets on standard workstations written in C++. It is available at https://gitlab.rlp.net/stnieble/raindrop.

2098-P: Transcriptomic Changes Associated with Oleate-Induced ß-Cell Proliferation in Rat Islets

Background: In nondiabetic obese individuals, β-cell mass accretion maintains glucose homeostasis by balancing levels of circulating insulin and insulin resistance. Type 2 diabetes appears when these compensatory mechanisms fail. Deciphering the pathways controlling β-cell proliferation has thus become a major research goal in the hope to identify therapeutic targets to expand β-cell mass and prevent or delay the onset of diabetes. Previously we demonstrated that fatty acids potentiate glucose induced β-cell proliferation in response to nutrient surfeit in rats. In recent studies we identified the mono-unsaturated fatty-acid oleate as a major fatty acid driving β-cell proliferation in rat islets ex vivo. Aim: To identify the transcriptional mechanisms underlying oleate-induced β-cell proliferation in rats. Method: Rat islets were exposed to 16.7mM glucose +/- oleate (0.5mM) for 48h. Proliferation was assessed by flow cytometry using c-peptide antibodies and EdU. Single cell cDNA libraries were generated using 10X Genomics technology and sequenced on the Illumina platform. Bioinformatic analyses were performed using the Cell Ranger pipeline, Seurat and Monocle. Results: Oleate induced a 3.1+/-0.4 fold increase (p<0.01; n=4) in β-cell proliferation. Following single-cell RNA sequencing, t-SNE plots revealed several β-cell sub-populations, including proliferating β-cells. Comparison between proliferative and non-proliferative β-cells identified more than 300 differentially expressed genes. Gene ontology suggested an increase in mitochondrial activity and reactive oxygen species (ROS) in proliferating β-cells. Pseudotime ordering of β cells revealed that ER-stress related genes are up-regulated prior to proliferation in response to oleate. Conclusion: Our study suggests an involvement of ROS and ER-stress in oleate-induced β-cells proliferation. Further functional analyses will substantiate the importance of these stress responses. Disclosure A. Vivoli: None. A. Castell: None. A. Pacis: None. R.M. Sladek: None. J. Ghislain: None. V. Poitout: None.

TCR-engineered T-cell therapy through personalized identification of CDR3 clonotypes.

e15021 Background: T-cell receptor (TCR)-engineered T cells are a novel option for adoptive cell therapy used for the treatment of several advanced forms of cancers. Unlike many shared tumor-specific antigens, such as melanoma-associated antigen (MAGE)-A3, MAGE-A4, and New York esophageal squamous cell carcinoma (NY-ESO)-1, neoantigen has garnered much attention as a potential precision immunotherapy. Personalized neoantigen selection serves a broader and more precision future for cancer patients. Methods: Dendritic cells (DCs) derived from adherent monocytes were pulsed with mixed peptides during the maturation phase. CD8+ cells positively selected from PBMCs were incubated with washed DCs. After 21day culture in X-VIVO medium with IL-7 and IL-15, cells were harvested and stimulated with peptides for 6 h. CD137+ cells were sorted by flow cytometric and immediately processed using the 10x Genomic Chromium Single Cell 5' Library & Gel Bead Kit and Chromium Single Cell V(D)J Enrichment Kit. The T-cell TCR libraries were constructed and sequenced on the Illumina HiSeq X Ten platform. The sequencing reads were aligned to the hg38 human reference genome and analyzed using the 10x Genomics Cell Ranger pipeline. The paired TCR α and β chain sequence of each cell was demonstrated with V(D)J analysis. TCR-T cells were constructed using the information of neoantigen specific TCR, and infused to patients. Results: Two patients were treated with the personalized TCR-T treatment. At the first stage, specialized immune cells were harvested and proceeded to single-cell TCR profiling. Then, the single cell sequencing of the first patient's sample revealed the top five neoantigen specific TCR CDR3 clonotypes with the proportion of 25%, 7.67%, 4.81%, 2.79%, and 2.54%, respectively. Similarly, the other patient had the top five TCR CDR3 sequenced with the proportion of 13.38%, 7.04%, 4.21%, 2.83%, and 1.94%, respectively. The results demonstrated that both patients had one or two dominant CDR3 clonotypes, which might reflect the strength of neoantigen in vivo. At the third stage, TCR-T cells were constructed, and infused to the patients. The clinical outcome will be evaluated in the near future. Conclusions: We have generated a pipeline for a highly personalized cancer therapy using TCR-engineered T cells. Although some questions remain to be answered, this novel approach may result in better clinical responses in future treatment.

SAT-314 Pituitary and Neuroendocrine Tumors Exhibit Distinct Transcriptomes on Single Cell RNA Sequencing

Neuroendocrine tumors (NETs) comprise a group of complex heterogeneous and increasingly prevalent neoplasms. They arise from diverse body regions, share derivation from primitive neuronal stem cells, secrete various bioactive peptides and exhibit a wide spectrum of tumor behavior from relatively indolent to rapidly progressive. Pituitary tumors represent 20% of intracranial tumors and share some features of NET histo-pathologic morphology, their excess hormone production and some have proposed re-naming pituitary tumors as PitNETs. However there has been no precise cell type composition comparison at the whole genome level in these two types of tumors. To explore intratumoral heterogeneity at a single cell resolution in pituitary and pancreatic NETs, we employed single cell RNA sequencing (scRNA-seq) to analyze in parallel cell populations from surgically resected pituitary tumors (n=4) and pancreatic NETs (pNET, n=2) using a 10x Genomics platform (v3). Using Cell Ranger pipeline for alignment and mapping, we obtained an average of 8,306±2,519 cells/sample with 1,516±822 genes/cell (mean reads/cell 67.9±30K). Seurat v3 was then used for read pre-filtering, normalization, and cluster identification. We identified 5 genes commonly expressed in both pNET and pituitary tumor populations, namely CALY (clathrin light chain binding GO:0032051), SPINT (peptidase inhibitor activity GO:0030414), CHGB (hormone activity GO:0005179), SCG5 (GTP binding GO:0005525) and SEZ6L2. As proof of their oral epidermal embryonic origin, the commonly expressed genes in 4 pituitary tumor samples include pituitary tissue restricted transcription factors (POU1F1, BEX1/2 GO:0033613), GNAS (G-protein beta/gamma-subunit complex binding GO:0031683), NLRP1 (peptidase activator activity involved in apoptotic process GO:0016505), PTN (protein phosphatase regulator activity GO:0019888) and ATP6V1G1 (ATPase binding GO:0051117). In parallel, 71 genes were present at high levels in pNET and involved in molecular functions such as calcium ion binding (HSPA5; ENPP2; C2CD4B; CALR; HSP90B1 GO:0005509) and endopeptidase inhibitor activity (PCSK1N; WFDC2 GO:0004866). Although our exploratory findings are limited to 6 samples, the robust commonly expressed genes within each tumor type clearly separated pituitary and pancreatic neuroendocrine tumors into distinct entities. We did observe conservation of chromogranins and prohormone convertases in these two tumor types, but it only represented a very small portion of overlap in transcriptome in pNETs. In summary, our findings suggest that pituitary and neuroendocrine tumors have only limited common molecular features, and by and large they stand as separate biologic populations. Our observations may begin to provide insight into the differences in tumor progression that are encountered clinically between pituitary tumors and pancreatic NETs.

PD47-04 SINGLE CELL RNA-SEQUENCING OF FRESH SUPERFICIAL NON-MUSCLE INVASIVE BLADDER CANCER DEMONSTRATES GENE EXPRESSSION PATHWAY DIFFFERENCES BETWEEN HIGH AND LOW GRADE TUMORS

You have accessJournal of UrologyBladder Cancer: Basic Research & Pathophysiology IV (PD47)1 Apr 2020PD47-04 SINGLE CELL RNA-SEQUENCING OF FRESH SUPERFICIAL NON-MUSCLE INVASIVE BLADDER CANCER DEMONSTRATES GENE EXPRESSSION PATHWAY DIFFFERENCES BETWEEN HIGH AND LOW GRADE TUMORS Stanley Malkowicz*, Brian Piening, Alexa Dowdell, David Walls, Nik Nair, Baoli Chang, and Brendan Keating Stanley Malkowicz*Stanley Malkowicz* More articles by this author , Brian PieningBrian Piening More articles by this author , Alexa DowdellAlexa Dowdell More articles by this author , David WallsDavid Walls More articles by this author , Nik NairNik Nair More articles by this author , Baoli ChangBaoli Chang More articles by this author , and Brendan KeatingBrendan Keating More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000934.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Non-muscle invasive urothelial cancer is a heterogeneous disease with a variable history and differential response to chemotherapeutic and immunologically based therapy. Understanding of the molecular pathogenesis of NMIBC has expanded yet remains incomplete. Recent advances in single-cell RNA sequencing has yielded compelling insight into the biology of tumorigenesis and pathology of several malignancies and may provide greater understanding of this condition. The objective of this study was to demonstrate the feasibility of single cell analysis of fresh NMIBC specimens and initially compare the heterogeneity of low and high grade lesion metabolic pathway expression. METHODS: We sequenced 65,000 cells per tumor from three low grade Ta and three high grade Ta human specimens. Single cell libraries were prepared using the 10X Genomics 3gene expression assay(v2) and high throughput sequencing was performed on a NovaSeq6000 to generate approximately 600 million reads-with close to 700 genes profiled per cell. 90% of reads mapped confidently to the human genome. Reads were processed using Cell Ranger (v2.2) and used Loupe Cell Browser (2.0) and Seurat(v2.2) to aggregate all data sets within and across the samples to perform differential gene expression analysis. RESULTS: Using tSNE automated unbiased clustering of single cell gene expression we identified key subsets of carcinoma cells that were associated with markedly different functional states. Pathway enrichment increased several fold in the direction of the higher grade lesions. CONCLUSIONS: Initial efforts at single cell RNA sequencing of NMIBC fresh tumors appears feasible with robust yields of cells and high quality control processing. Differences were noted in high grade and low grade lesions in the intensity of expression of several metabolic pathways including cellular oxidation and regulation of cell death with enrichment in the direction of higher grade lesions. Further analysis is warranted which may provide compelling insights into the heterogeneity and pathology of NMIBC. Source of Funding: The Castleman Family Research Fund, Gary Johnson Research Fund and Bruce Shelley Research Fund. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e926-e926 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Stanley Malkowicz* More articles by this author Brian Piening More articles by this author Alexa Dowdell More articles by this author David Walls More articles by this author Nik Nair More articles by this author Baoli Chang More articles by this author Brendan Keating More articles by this author Expand All Advertisement PDF downloadLoading ...

Abstract 7: Nerve Growth Factor Derives From Pericytes And Smooth Muscle Cells After Extremity Trauma

Purpose: Neurotrophic factors like nerve growth factor (NGF) have been described in the literature as a crucial regulator in developmental biology and inflammation of musculoskeletal tissues. While extremity trauma has been shown to elicit re-engagement of developmental programs like NGF upregulation and invasion of new sensory nerve endings in a mature organism, the source of NGF has remained largely unknown. In a validated extremity trauma mouse model, we have previously demonstrated a significant attenuation of injury site re-innervation by sensory fibers following inhibiting of afferent neural signaling and NGF binding of its receptor TrkA. Given this significant impact of NGF on sensory reinnervation of injured soft tissues and downstream bone formation, we examined the source of NGF after extremity trauma using single cell transcriptomic and reporter proteomic technologies. Methods: A 30% dorsal burn and Achilles transection was performed. The tendon site tissues were harvested from baseline (t0) and day 3, 7, and 21 (n=3-4/group) after induction. Samples were prepared for library generation on a 10x Genomics Chromium Controller, sequenced on a Illumina HiSeq 4000, and analyzed with Cell Ranger Software for pre-processing and alignment to the mm10 genome. Downstream analyses including unsupervised clustering and canonical correlation analyses were performed with Seurat. Immunofluorescent (IF) labeling of cellular markers including αSMA was performed using NGF-eGFP reporter mice at baseline, and at 1,3 and 9 weeks after injury (n=2-3/group). Results: To localize cell specific Ngf expression from injured soft tissue, 9 cell clusters were defined across all timepoints: mesodermal (Prrx1) populations including Acta2+ pericyte/vascular smooth muscle (SMC) and Pdgfra+ mesenchymal cells, two Pecam1+ endothelium, and four inflammatory cell populations (mixed, B cell, T cell, and neutrophil). Ngf was found uniquely enriched in the pericyte/SMC cluster in a composite view. These pericytes/SMCs were found in increasing number and Ngf expression across timepoints, peaking at day 21. To further characterize this joint cluster (1273 cells), the pericyte/SMC cluster was isolated and blindly re-clustered to produce new sub-clusters, distinguishing Pdgfrb (platelet derived growth factor receptor beta)highPrrx1highAbcc9+ pericytes from Acta2highPln+Myh11high SMCs; an uncharacterized cluster with 24 cells was discarded from analysis. Of these two sub-clusters, vascular SMCs demonstrated the highest Ngf expression at baseline (mean normalized Ngf expression of 0.52 [in 41% of cells] vs 0.28 [in 23% of cells]. IF of NGF-eGFP reporter mice show robust NGF co-localization with αSMA+ SMCs. Conclusions: This is the first work characterizing the pericyte and vascular SMC as a major contributor to NGF signaling at an extremity injury site. The fine resolution defining the cellular source of NGF provides insight into potential mechanisms correlating nascent nerve and vascular growth given the intersection of these pathways at the level of pericytes and myofibroblastic SMCs that will inform future candidate therapeutics to improve extremity trauma healing and re-innervation.

P038 A single-cell transcriptomics approach reveals high-resolution cellular signatures in Crohn’s disease

Abstract Background Single-cell RNA sequencing (sc-RNAseq) provides high-resolution analysis of individual cells and cell clusters within complex tissues like the intestinal mucosa. No study to date has applied this novel technology to understand the complexity of intestinal inflammation in Crohn’s disease (CD) patients. Our aim is to identify the cellular subsets present in healthy and inflamed colon, and to describe how individual subsets are regulated in active CD. Methods Colon biopsies from a healthy control (HC) and two patients with endoscopically active colonic CD were collected and processed by enzymatic digestion and cell sorting to discard dead cells. Single-cell suspensions were processed by droplet-based protocol 10× chromium system followed by library preparation and sequencing. Sequencing data were aligned and quantified using Cell Ranger. Downstream analysis was performed using the R package Seurat. Results scRNA-seq analysis identified 14 transcriptionally different cell subsets in the healthy colon, including diverse types of T cells, plasma cells, mesenchymal cells, epithelial cells and myeloid populations, as well as a subset of MHCII+ CD20+ B cells. Overall, the number of transcriptionally differentiated cellular subsets was greater (16 and 17 subsets per sample) in the two CD colonic samples revealing higher cellular heterogeneity in active CD. In particular, in the HC we identified three T-cell subsets including a CD4+, CD8+ and a smaller subset of intraepithelial lymphocytes. In contrast, samples from CD patients presented four to six clearly differentiated T-cell clusters depending on the patient, with a marked enrichment of cytotoxicity genes (GZMA, GZMB, NKG7, CCL5 and AOAH) and T-regulatory genes (FOXP3, CTLA4, BATF and IL2RA). In addition, the HC sample presented three differentiated subsets of antibody-secreting plasma cells with predominant IgA and IgM production. In contrast, a clear expansion of IgG-producing plasma cells was observed in both CD patients. Moreover, the profiles of specific subsets significantly changed revealing transcriptional regulation within the individual subsets. Specifically, macrophages within the HC mucosa expressed the M2 markers CD163 and CD209 together with a whole signature of 163 additional genes, while in CD, they lost expression of these markers and upregulated CXCL8 and IL1B which were co-expressed with 268 additional genes common to macrophage populations in both CD patients. Conclusion We show that sc-RNAseq can be applied to study at a high cellular resolution human intestinal biopsies. Understanding the transcriptomic profile of the CD-related subsets will help dissect disease pathophysiology and provide knowledge for therapeutic targets.

Analysis of Transcriptional Profiling of Immune Cells at the Single-Cell Level.

RNA sequencing has proven to be a key innovation for the study of biological processes by enabling scientists to measure differences in gene expression in different tissues.With recent advances in sequencing technology, researchers are able to measure gene transcription at the single-cell level, revealing previously unknown diversity and specificity of immune cells. The single-cell sequencing method now enables profiling of the T-cell receptor (TCR) genes resulting from V(D)J recombination.Here we describe how to adapt single-cell RNA sequencing data generated using the 10× genomics 5'V(D)J immune cell profiling workflow for integration into the R analysis pipeline.We will start with the data matrix files generated from the 10× genomics Cell Ranger alignment software and detail how to format this data as input for the R analysis package called Seurat such that data from both the overall cell transcript abundance and the targeted V(D)J transcript abundance data can be visualized on the same plots.

Mutations and Copy Number Gains of the BCL2 Family Members Mediate Resistance to Venetoclax in Multiple Myeloma (MM) Patients

Background: Selective BCL2 inhibition with venetoclax, induces deep responses in relapsed MM. However, acquired resistance to venetoclax frequently occurs. Importantly, in the Bellini trial, while the combination of venetoclax and bortezomib doubled the median progression free survival compared to control, it resulted in shorter overall survival. These results highlight the challenges secondary resistance poses and the need to investigate the mechanisms mediating the emerged resistance to venetoclax. Methods & Results: Bone marrow (BM) aspirates were collected from patients (n=8) treated with venetoclax prior to initiation of therapy and at disease progression. Mononuclear cell fractions were isolated through ficoll coupled with magnetic sorting of CD138+cells. Ex-vivo functional profiling of venetoclax sensitivity was performed on CD138+cells. Unbiased mRNA and DNA profiling was conducted by single-cell RNA-sequencing (scRNA-seq), single cell ATAC-seq and single cell copy number analysis (scCNV) using the GemCode system (10x Genomics). Cell Ranger, Seurat, Signac and Monocle were used for data analysis. Ex-vivo apoptosis studies revealed a dynamic shift of the IC50s of venetoclax from a median of 100 nM in pretreatment sample to >1000 nM at disease progression. Single cell CNV profiling identified a significant expansion of a pre-treatment subclonal (<1%) cluster of cells with 1q21 copy number gain to become the predominant clone (>70%) at the time of relapse. The scale of the 1q CNV gain varied from 3 Mb to a very focal gain of 100 kb encompassing the MCL1 gene locus. Of note copy number gain was also identified in the BCL2L1 gene, but no copy number losses were detected in the BAX or BAK gene loci. Single cell RNA profiling of the same samples confirmed the gain in the MCL1 mRNA transcript with downregulation of BCL2 at the time of relapse. Consistent with a branching evolutionary model, scATAC-seq revealed increased chromatin accessability studies at the MCL1 locus in subclones of relapsed samples. Cell trajectory analysis and pseudotime ordering of cells (Monocle) revealed the emergence of a highly proliferative clone and of an MCL1 dependent clone as the disease evolved from its original BCL2 dependent cluster at pseudotime (t0) (Figure). Furthermore, ex-vivo apoptosis profiling revealed a shift in the sensitivity of the CD138+cells with an acquired sensitivity to MCL1 inhibitor (S63845). These findings suggest that the 1q21 copy number gain with MCL1 upregulation is likely mediating the acquired venetoclax resistance. In order to functionally confirm whether the gain in MCL1 is sufficient to shift BCL2 to MCL1 dependency and induce resistant to venetoclax, we stably overexpressed MCL1 in the KMS12PE BCL2-dependent MM cell line (KMS12PE_MCL1) and examined its sensitivity to venetoclax and/or S63845 relative to control vector (KMS12PE_EV). Of interest, co-immunoprecipitation (co-IP) of BIM in KMS12PE_MCL1 demonstrated a shift in BIM loading and co-IP with MCL1 and BCL2 while it was restricted to BCL2 in KMS12PE_EV cells. Importantly, venetoclax IC50 of KMS12PE_EV increased by 200 folds in KMS12PE_MCL1 cells with acquired sensitivity to S63845. scCNV analysis of one patient did not identify any gain in the 1q locus at the time of disease progression to venetoclax. Mutation analysis however identified a de novo BCL2 mutation [NM_000633.2:c.332A>C, p.(Asp111Ala)]. To determine whether the Asp111Ala mutation alone is sufficient to confer resistance to venetoclax, the mutant was overexpressed in KMS12PE cells. While BIM binding to BCL2 was unaffected, Aps111Ala largely abrogated venetoclax-induced BIM displacement from BCL2 and reduced KMS12PE cells sensitivity to venetoclax by ~7.5 folds. Structure modeling also demonstrated the inability of venotoclax to tightly bind mutant BCL2_Asp111Ala hydrophobic groove. Conclusion: We have discovered and functionally characterized a novel BCL2 mutation that confers in vitro and in vivo resistance to venetoclax-treated MM patients. In addition we have identified through single cell profiling an enrichment of MM clones with MCL1 locus copy number gain at the time of acquired venetoclax resistance. Early detection and dynamic monitoring of these abnormalities (BCL2 mutant or 1q gain) with early therapeutic interventions targeting these clones may enhance venetoclax efficacy and improve patients' survival. Figure Disclosures Neri: Celgene, Janssen: Consultancy, Honoraria, Research Funding. Boise:Genentech Inc.: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Research Funding. Bahlis:Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria.

Single Cell Profiling of DNMT3A-Mutant Progenitors Reveals LY86 As a Novel Pre-Leukemia Marker and Potential Therapeutic Target

Acute Myeloid Leukemia (AML) is a clonal disease of the hematopoietic system that initiated and sustained by self-renewing hematopoietic stem and progenitor cells (HSPC). Mutations in the de novo DNA methyltransferase 3A (DNMT3A) gene occur in approximately 25% of adult acute myeloid leukemias (AML). Although the mechanisms through which such mutations promote leukemogenesis remain unclear, we have previously shown that loss of the DNMT3A can inhibit normal hematopoietic differentiation (Challen, Nature Genetics, 2011), accounting for the emergence of DNMT3A-HSC clones as a predisposition to hematological malignancies (Yang, Cancer Cell, 2015). Therapies that selectively eliminate the initiating pre-leukemic population would greatly improve outcomes for affected patients. However, the identification as well as selective elimination of such a distinct population has been problematic because of the considerable overlap in gene expression profiles with bulk normal hematopoietic stem cells. Molecular targets during leukemia development have not been well elucidated due to lack of the real definitive markers, which is a significant knowledge gap and barrier for understanding clonal leukemogenesis and therapeutic applications. Single-cell RNA sequencing has emerged as a powerful tool to analyze new cell types, cellular heterogeneity and cell differentiation routes. This technique made important contributions to our understanding of hematopoietic stem and cancer cell heterogeneity and selective resistance of cancer cell subpopulations to molecularly targeted cancer therapies. To identify early events involved in pre-leukemic transformation, we have performed single-cell RNA-sequencing (scRNA-seq) in WT and Dnmt3a KO mice. Flow cytometry sorted wild-type and pre-leukemic Dnmt3a KO HSPC cells were captured using 10X genomics chromium platform. After genome mapping, dimensional reduction, and clustering using Cell ranger pipeline, we generated transcriptome data and integrated the data sets using Seurat. Approximately 8,000 cells from each group were sequenced, and each cell expressed 1800-4500 genes. Graph-based clustering analysis revealed 16 unique cell clusters in both WT and DNMT3A KO mice. Interestingly, when compared with WT mice, we observed a 10-fold expansion of a single cell cluster in Dnmt3a KO cells before the advent of overt leukemia. This cluster co-expresses several stem cell genes including well-known leukemic stem cell surface markers such as CD47, as well as several novel genes. Some of these novel genes, encode cell surface proteins such as Ly6c2 and Ly86. We further validated protein expressions in AML cell lines and primary AML blast. In conclusion, the discovery of novel cluster in DNMT3A KO mice, and the relative abundance of this cluster in pre-leukemic stage of DNMT3A KO mice indicates that they promote leukemogenesis and offers an opportunity to specifically target DNMT3A mutant pre-leukemic cells using T cell immunotherapy. Disclosures No relevant conflicts of interest to declare.

Single Cell Sequencing Reveals Heterogeneity of Gene Expression in KMT2A Rearranged Infant ALL at Relapse Compared to Diagnosis

Introduction Infant acute lymphoblastic leukemia (ALL) with KMT2A rearrangement (KMT2A-r) is associated with a very poor prognosis. Disease free survival from the date of diagnosis is approximately 20% to 40%, depending on age, white blood cell count, and response to induction therapy. Refractory and relapsed infant ALL is often resistant to attempts at re-induction, and second remission is difficult to both achieve and maintain. Genomic sequencing studies of infant KMT2A-r ALL clinical samples have demonstrated an average of fewer than 3 additional non-silent somatic mutations per case at diagnosis, most commonly sub-clonal variants in RAS pathway genes. We previously reported relapse-associated gains in somatic variants associated with signaling, adhesion, and B-cell development pathways (Blood 2016 128:1735). We hypothesized that relapsed infant ALL is characterized by recurrent, altered patterns of gene expression. In this analysis, we utilized single cell RNA sequencing (scRNAseq) to identify candidate genes with differential expression in diagnostic vs. relapse leukemia specimens from 3 infants with KMT2A-r ALL. Methods Cryopreserved blood or bone marrow specimens from 3 infants enrolled in the Children's Oncology Group AALL0631 trial were selected for analysis. Samples from both diagnosis (DX) and relapse (RL) time points were thawed and checked for viability (>90% of cells viable) using trypan blue staining. Samples were multiplexed and processed for single cell RNA sequencing using the Chromium Single Cell 3' Library Kit (v2) and 10x Genomics Chromium controller per manufacturer's instructions (10x Genomics, Pleasanton, CA). Single cell libraries were converted to cDNA, amplified, and sequenced on an Illumina NovaSeq instrument. Two technical replicates were performed. Samples were de-multiplexed using genotype information acquired from previous whole exome sequencing (WES) and demuxlet software. Transcript alignment and counting were performed using the Cell Ranger pipeline (10x Genomics, default settings, Version 2.2.0, GRCh37 reference). Quality control, normalization, gene expression analysis, and unsupervised clustering were performed using the Seurat R package (Version 3.0). Dimensionality reduction and visualization were performed with the UMAP algorithm. Analyses were restricted to leukemia blasts with CD19 expression by scRNAseq. Results The clinical features for each case are shown in Table 1. Cells from the 3 infant ALL samples clustered together, distinct from cells of non-infant B-ALL, T-ALL, and mixed lineage acute leukemia biospecimens in the Children's Mercy scRNAseq database, but largely did not overlap with one another. For each of the 3 infant cases, cells from DX and RL time points could be distinguished by differential patterns of gene expression (Figure 1). Individual genes with statistically significant (p<0.05) log-fold change values were examined. Figure 2 summarizes the number of genes with up-regulation of expression by scRNAseq at RL compared to DX. Only 6 genes, DYNLL1, HMGB2, HMGN2, JUN, STMN1, and TUBA1B, were significantly increased at RL across all 3 cases. We repeated this analysis, restricting to leukemia blasts with CD79A expression, and identified these same 6 genes, and 4 additional genes: H2AFZ, NUCKS1, PRDX1, and TUBB, as consistently up-regulated in RL clusters. We examined the expression of candidate genes of interest, including clinically targetable genes, to compare the distribution of expression at DX and RL (Table 2). Conclusion Genomic factors underlying the aggressive, refractory clinical phenotype of relapsed infant ALL have yet to be defined. Each of these 3 cases demonstrates unique expression patterns at relapse, readily distinguishable from both the paired diagnostic sample and the other 2 relapse samples. Thus, scRNAseq is a powerful tool to identify heterogeneity in gene expression, with the potential to discover recurrent genomic drivers within resistant disease sub-clones. Ongoing analyses include scRNAseq in additional infant ALL samples, relative quantification of transcript expression in single cells, and comparison with bulk RNAseq data. Disclosures No relevant conflicts of interest to declare.

COMP-06. PROBING GLIOBLASTOMA AND ITS MICROENVIRONMENT USING SINGLE-NUCLEUS AND SINGLE-CELL SEQUENCING

Abstract Single-cell (scSeq) and single-nucleus sequencing (snSeq) are powerful tools to investigate cancer genomics at single cell resolution. Multiple studies have recently illuminated intratumoral heterogeneity in glioblastoma, however, the majority focused on molecular complexity of tumor cells, without taking into account unexplored host cell types that contribute to the microenvironment around tumor. To address the glioblastoma microenvironment composition and potential tumor-host interactions, we performed deep coverage sequencing of freshly resected primary GBM patient tissue without implementing any tumor enrichment strategies. The sequencing resulted in 902 cells and 1186 nuclei, respectively, passing quality control and with low mitochondrial gene percentage. We customized reference transcriptome by listing gene transcript loci as exons to take into account immature RNA, which greatly improved the alignment rate for single-nucleus data. We applied Cell Ranger pipelines (Version 3.0.2) and Seurat package (Version 2.3.1) and discovered 10 clusters in both scSEQ and snSEQ. Pathway analysis of each cluster signature in scSeq data along with known GBM microenvironment cell signatures revealed glioma tumor population along with surrounding microglia/macrophages, astrocytes, pericytes, oligodendrocytes, T cells and endothelial cells. The analysis of snSEQ was able to capture the majority of cell types from patient tissues (tumor and microenvironment cells), but interestingly presented different cell type composition in microenvironment cell types such as microglia/marcophages. Integrating single-cell and single-nucleus transcriptomic data using canonical correlation analysis facilitated a comparison of snSEQ and scSEQ, contrasting depiction for certain cell types (e.g. NKX6-2 gene in Oligodendrocytes). Differential analysis of pathways between tumor and microenvironment cells unveiled potentially rewired pathways such as double strand break repair pathway. Our results demonstrate the cellular diversity of brain tumor microenvironment and lay a foundation to further investigate the individual tumor and host cell transcriptomes that are influenced not only by their cell identity but also by their interaction with surrounding microenvironment.

1315PD - Unsorted single-cell RNA sequencing profiles of metastatic melanoma patients reveal the heterogeneity of melanoma-associated fibroblasts

BackgroundSingle cell transcriptomic analyses of the tumor microenvironment provides unprecedented opportunities to guide precision immune therapy of melanoma patients. Previous single-cell RNA sequencing studies in metastatic melanoma patients have shown that the TME composition can be deciphered with a FACS-based single cell assay. Here, we applied droplet-based single-cell RNA sequencing for the decomposition of the tumor microenvironment of metastatic melanoma patients before PD1-based immune therapies and, when possible, during or after PD1 therapy. MethodsFresh biopsies and surgical excisions from melanoma patients destined for standard-of-care immune therapy were collected and processed within 2hours. To capture the global cell composition, cells were not FACS sorted. We performed single-cell RNA sequencing using the 10x Chromium platform. We used both 3’ and 5’ RNA sequencing. Libraries were sequenced at the Campus Biotech platform in Geneva. Standard analytical pipelines were applied (Cell Ranger 3.0, Seurat 2.0). ResultsOur current dataset comprises a total of 26 tumors from 18 patients with a total number of 66’923 cells. The mean number of cells analyzed per patient was 2573 (Range: 160-12’676). Immune TME cell types could be readily identified, but with a significant patient-to-patient heterogeneity. Importantly, melanoma-associated fibroblasts (MAFs) were observed in most tumors (23/26), with an abundance of>1% in 19 out of 26 tumors. The MAF population, which in total comprised 4722 cells (median 2.1%/tumor, range: 0-59.8%) was heterogeneous and was composed of at least two different sub-clusters. The MAF subtypes were correlated with outcomes from PD1 therapy, with an immune evasion gene signature, and with bulk TCGA melanoma data, as will be presented. ConclusionsUnsorted single-cell RNA sequencing of fresh human melanoma samples demonstrably provides an insightful snapshot of heterotypic cell compositions. In contrast to previous FACS-based scRNAseq assays, both immune and non-immune components of the tumor microenvironment are efficiently captured. MAFs form a sizeable and heterogenous component of melanoma tumors, heretofore underappreciated in melanoma. Legal entity responsible for the studyCHUV. FundingSystemsX. DisclosureAll authors have declared no conflicts of interest.

Abstract 3757: Probing glioblastoma and its microenvironment at single cell resolution

Abstract Single-cell sequencing (scSeq) is a powerful tool to investigate cancer genomics at single cell resolution. Multiple studies have recently illuminated intratumoral heterogeneity in glioblastoma, however, the majority focused on molecular complexity of tumor cells, without taking into account unexplored host cell types that contribute to the microenvironment around GBM tumor. To address the glioblastoma microenvironment composition and potential tumor-host interactions, we performed deep coverage (176k average reads per cell) scSeq of freshly resected primary GBM patient tissue without implementing any tumor cell enrichment strategies. scSeq libraries for 902 cells were prepared using 10X Gemcode platform and sequenced on Illumina NextSeq 500. This run was of high quality with 2,663 median genes per cell and low mitochondrial gene percentage (median < 5%). We used Cell Ranger analysis pipelines and Seurat packages to classify individual cells into 10 clusters and visualize them using t-SNE two-dimensional projections. We then identified the signature gene set for each cluster, relative to all other cells. Pathway analysis of each cluster signature along with known GBM microenvironment cell signatures revealed glioma tumor population along with surrounding microglia/marcophages, astrocytes, pericytes, oligodendrocytes, T cells and endothelial cells. Cell type markers identified by single cell transcriptomics were validated by IHC analysis. Microenvironmental composition and single cell signature will be confirmed through single nuclei sequencing of preserved (Frozen) tumor sample. Our results demonstrate the cellular diversity of brain tumor microenvironment and lay a foundation to further investigate the individual tumor and host cell transcriptomes that are influenced not only by their cell identity but also by their interaction with surrounding microenvironment. Citation Format: Sen Peng, Sanhita Rath, Saumya Bollam, Jenny Eschbacher, Shwetal Mehta, Nader Sanai, Michael Berens, Seungchan Kim, Harshil Dhruv. Probing glioblastoma and its microenvironment at single cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3757.

Abstract 130: Characterization of the tumor microenvironment using single cell transcriptomics of triple negative breast cancer allografts treated with doxorubicin

Abstract It is currently unclear how stromal components affect drug response and the emergence of drug resistance, in primary tumors. To determine how individual cells within the stroma of triple negative breast cancer (TNBC) allografts respond to chemotherapy, we used single cell sequencing to profile individual cells present in murine tumors with or without exposure to doxorubicin (Dox). Dox is a common chemotherapeutic agent used to treat breast cancer which inhibits breast cancer proliferation by intercalating into DNA and preventing topoisomerase II activity. Several autonomous processes have been implicated in the development of chemoresistance yet the impact of stromal and immune cells on tumor progression is still poorly understood. In this study, TNBC 4T1 cell line were utilized to generate murine allograft tumors in immunocompetent BALB/c mice. Tumor composition was analyzed via single cell RNA sequencing after 3 and 7 days of doxorubicin chemotherapeutic regiment mimicking clinical treatment. Using Cell Ranger single cell software suite and Seurat R toolkit, single cell transcriptomic analysis identified the cellular composition of tumors through expression of cell-type specific genes. Stromal cell types such as endothelial, fibroblast and epithelial cells were assessed and quantified in the tumor microenvironment. Immune cell types including neutrophils, monocytes, macrophages, T-cells and B-cells were also identified in the stroma and the responses to doxorubicin treatment was determined based on the gene expression changes. In this study, cancer-associated fibroblasts and non-canonical tumor associated macrophage subpopulations are of particular interest. As expected, we found both qualitative and quantitative changes in specific subpopulations of stromal cells in response to Dox exposure. Identification of stromal and immune cell sub-types could also lead to improved diagnostic capabilities and tumor susceptibilities. Future studies modulating non-cancerous cells in the tumor microenvironment may increase efficacy of chemotherapeutics. Further elucidating the specific cellular subpopulations within the tumor microenvironment that shift in response to drug exposure may provide new therapeutic avenues. Understanding changes in cell populations within the drug exposed tumor microenvironment can aid in future drug development to specifically target cells least sensitive to chemotherapy exposure. This study received funding from LLNL LDRD grant 19-SI-003. This work was conducted under the auspices of the USDOE by LLNL (DE-AC52-07NA27344). Citation Format: Nicholas Hum, Aimy Sebastian, Sean Gilmore, Elizabeth K. Wheeler, Matthew A. Coleman, Gabriela G. Loots. Characterization of the tumor microenvironment using single cell transcriptomics of triple negative breast cancer allografts treated with doxorubicin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 130.

PF156 SINGLE-CELL TRANSCRIPTIONAL HETEROGENEITY SUGGESTS NOVEL FINGERPRINT OF RELAPSE IN ACUTE MYELOID LEUKEMIA

Background: Some rare subgroups of leukemia cells harboring relapse-inducing genes were selected after chemotherapy. Tounravel intra-tumoral heterogeneity and selective drug-resistance, single-cell RNA sequencing (scRNA-seq) was already performed on many solid tumors and blood cancer to achieve the high-resolutiontranscriptome profiling on individual cells from a larger heterogeneous population. However,the comprehensive investigation on cancer heterogeneityduring cancer development at single-cell resolution is still rare. Aims: To identify diverse subsets and molecular characteristics of acute myeloid leukemia (AML) relapse Methods: Since single-cell suspension was obtainedfrom bone marrow of acute myeloid leukemia samples, we used the 10x GenomicsChromium platform to capture transcriptomes of singlecells on barcoded mRNA capture beadsfor massively parallel scRNA-seq. Data processing followed by the Cell Ranger software pipelineto demultiplex cellularbarcodes, and map reads to the genome and transcriptome hg38 using the STAR aligner. Uniquemolecular identifier (UMI) count matrix and quality control were performed using Seurat. The t-SNE map was calculated using Rtsne package Results: We analyzed transcriptome data from near 50K single leukemia bone marrow cells across 3 patients during newly diagnosed, complete remission and relapse stages. To define the landscape of cellular heterogeneity and its association with outcome in an unbiased manner, we performed unsupervised machine learning algorithm on near 50K single cells from leukemia bone marrow and identify one robust 14-cluster solution (from 0 to 13, Figure 1A) and the hallmark genes within each clusters (Figure 1B). The pattern exhibits distinct distribution on different stages (Figures 1C), indicating intra-tumoral heterogeneity during leukemia progression. Within cluster 0, the subgroups expressing such as LILRB2, TNFAIP2 or PTAFR were chemotherapy sensitive (Figure 2A). While the subgroups expressing such as APOC1, CDKN2A, KLF1 or GATA1 were chemotherapy resistant (Figure 2B). These chemotherapy resistant subgroups may play some key roles in leukemia relapse.Summary/Conclusion: We identify novel relapse subgroups in highly heterogeneous leukemia, which could not be found using traditional bulk RNA-seq.

Scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data

BackgroundSingle-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking.ResultsHere we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package.Conclusionsscruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.

Deconstructing pulmonary fibrosis at single‐cell resolution

RationaleDespite years of research, the fundamental mechanisms driving the pathogenesis of pulmonary fibrosis remain unclear. Numerous histopathologic patterns of pulmonary fibrosis have been identified in association with different patterns of risk factors, but to date it remains unclear as to what mechanisms are shared across different forms of pulmonary fibrosis and which drive distinct pathologies and outcomes. We hypothesize that using single‐cell transcriptomic approaches, we can decipher both the conserved and distinct mechanisms driving pulmonary fibrosis phenotypes.MethodsAt the time of lung transplantation, single‐cell suspensions were generated from the lung parenchyma of pulmonary fibrosis patients and from declined donor lungs (controls). Unsorted single‐cell suspensions and CD45 depleted fractions were used for scRNA‐seq. Single‐cell RNA‐sequencing library preparation was performed using the 10X Genomics 3′ or 5′ kit, and sequencing was performed on an Illumina hiSeq4000 or Novaseq. Following alignment, demultiplexing was performed using Cell Ranger. Graph‐based clustering and scRNA‐seq analysis was performed using the Seurat package in R. Developmental lineage reconstruction was performing using Monocle and p‐Creode. Diagnoses were assigned based on clinical interpretation of explant pathology.ResultsJoint graph‐based clustering and canonical correlation analysis of scRNA‐seq profiles from >30,000 cells from control (n=7), IPF (n=7), chronic hypersensitivity pneumonitis (cHP, n=4), and nonspecific interstitial pneumonia (NSIP, n=3) identified 21 distinct clusters, representing the major known subtypes in the lung, as well as numerous intermediate/transitional cell types and/or states. Strikingly, across pulmonary fibrosis phenotypes, collagen and ECM gene expression was highly enriched in ACTA2low, PDGFRa+ fibroblasts, while collagen and ECM gene expression were lower in ACTA2hi myofibroblasts; few collagen‐expressing inflammatory or epithelial cells were identified. A series of cell types co‐expressing AT1 and AT2 markers were identified in both fibrotic and control lungs. Compared to NSIP and cHP, IPF epithelial cells demonstrated increased senescence markers.ConclusionsAcross different histopathologic forms of pulmonary fibrosis, both shared and divergent pathologic gene expression programs can be identified. Further study is needed to more comprehensively define the common gene expression programs that drive progressive pulmonary fibrosis.Support or Funding InformationThis work was supported by NIH/NHLBI, the Doris Duke Charitable Foundation, and Boehringer Ingleheim.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Identification and Characterization of Cellular Heterogeneity within Human Late Developmental Stage Dissociated Lung by CITE‐Seq

A developmental pattern of lung cell maturation known to occur in late gestation and postnatal life remains poorly understood in humans. This study demonstrates sub‐population enriched, as well as single cell, transcriptome analyses with and without surface protein cell‐indexing, to assess cell specific expression in developing lungs obtained from neonatal and pediatric donors.MethodsWe isolated four major lung cell types (epithelial [EPI], endothelial [END] and non‐endothelial mesenchymal [MES] cells and leukocytes [MIC]) using fluorescence‐activated cell sorting from donor lungs. Next‐generation sequencing libraries were prepared from sorted cell RNA. Similarly, transcriptional profiles of 5500 newborn human lung cells were generated using single cell RNA sequencing (sc‐RNAseq) and CITE‐seq techniques. Cell ranger v2.2.0 and CITE‐seq‐Count (v1.3.1a) were used to process transcriptional and surface protein data for each cell. Downstream analysis was performed within Seurat (v2.3) and the R 3.5.0 environment. Low quality cells were removed including if >12.5% mitochondrial transcription or < 500 UMI/cell. Hyper variable genes were detected and cells were visualized using a tSNE plot. Clusters were identified and annotated based on cell‐type cluster specific marker genes associated with the Lung GENS database (Thorax. 2015; 70:1092).ResultsBulk RNAseq confirmed developmental cell‐specific patterns of gene expression. On single cells, overlaying surface protein quantification onto cell type annotated clusters in tSNE space shows good RNA and protein signature agreement. For example, CD31 marks an endothelial cluster with probable substructure based on differential gene expression of VWF, HPGD, and CDH5. Likewise, EpCAM was restricted to a tight cluster consistent with lung EPIs, a portion of which co‐expressed podoplanin, consistent with type I alveolar EPIs. In addition, CD45+ marked leukocyte clusters. Defining a cell as CD45+ if surface protein quantification for CD45 was >2, created a subset of 1,138 cells further delineated by RNA into those consistent with NK T, B and T cells and a second larger myeloid cluster. Based on co‐expression of genes used to identify pulmonary macrophages, CD11b(ITGAM), CD206(MRC1), CD169(SIGLEC1) and CD15(FUT4), 10% of the myeloid cluster were consistent with monocytes (FUT4− ITGAM+MRC1−SIGLEC−), 4% interstitial macrophages (FUT4−ITGAM+MRC1+ SIGLEC−) and 0.01% alveolar macrophages (FUT4−ITGAM+MRC1+SIGLEC+).ConclusionsWe have demonstrated surface marker indexing of neonatal human lung cell subpopulations, consistent with non‐biased single cell transcriptome content, identifying differentiated antigen presenting cells and great potential to finely identify further cell subsets in early postnatal lung.Support or Funding InformationFunding: NHLBI U01HL122700This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Single Cell RNA Sequencing and V(D)J Profiling of T-Cell Large Granular Lymphocytosis

Introduction. T-cell large granular lymphocytosis (T-LGL) is a low grade lymphoproliferative disorder, often clinically manifest as bone marrow failure. Treatment with immunosuppressive therapies is effective, but the dominant clone may persist even in responding patients. The pathogenesis of T-LGL has not been fully elucidated. In this study, we performed single cell RNA sequencing (sc-RNA seq) and V(D)J profiling to discern clonotypes and gene expression patterns of T lymphocytes from T-LGL patients who were sampled before and after treatment.Methods. Blood was obtained from patients participating in a phase 2 protocol of alemtuzumab as second line therapy (NCT00345345; Dumitriu B et al, Lancet Haematol 2016). Leukapheresis was performed in 13 patients (M/F 7/6; median age 51 years, range 26-85) before and after 3-6 months alemtuzumab administration and in 7 age-matched healthy donors. Cryopreserved blood was enriched for T cells with the EasySep Human T cell Isolation Kit (Stem cell). sc-RNA seq was performed on the 10XGenomics Chromium Single Cell V(D)J + 5' Gene Expression platform, and sequencing obtained on the HiSeq3000 Platform. Barcode assignment, alignment, unique molecular index counting and T cell receptor sequence assembly were performed using Cell Ranger 2.1.1.Results. Four hundred fifty thousand cells from 13 patients and 107,000 cells from 7 healthy donors were profiled. We measured productive TCR chains (which fully span the V and J regions, with a recognizable start codon in the V region and lacking a stop codon in the V-J region, thus potentially generating a protein). We detected at least one productive TCR α-chain in 50%, one productive TCR β-chain in 69% and paired productive αβ-chains in 47% of all cells. There was loss of TCR repertoire diversity in patients which was quantified by Simpson's diversity index; most patients showed oligoclonal or, less frequently, monoclonal expansion of the TCR repertoire (Fig. A). Regardless of clinical response, alemtuzumab treatment did not correct the low TCR repertoire diversity. TCR repertoires can be classified as “public”, when they express identical TCR sequences across multiple individuals, or “private”, when each individual displays distinct TCR clonotypes. No TCRA or TCRB CDR3 homology among patients was observed: most TCR clonotypes appeared to be private. Our data suggests that T-LGL is etiologically heterogenous disease, consistent with T cell expansion in response to a variety antigens, in diverse HLA contexts, or randomly. Despite differences of TCR among patients and healthy donors, and the presence of large clones in patients, distribution of TCR diversity followed the power law distribution in healthy donors and patients (Fig. B, showing the negative linear relationship between logarithmic expression of clone frequency and clone size). The observed distribution is consistent with a somatic evolution model, in which cell fitness depends on cellular receptor response to specific antigens and stimulation of cells by cytokine and other signals from the environment; fitted clones have higher birth-death ratios and thus expand (Desponds J et al, PNAS 2016).CD4 and CD8 T cells can be virtually separated by imputation from their transcriptomes (Fig. C). Comparison of gene expression between patients and healthy donors showed dysregulation of genes involved in pathways related to the immune response and cell apoptosis, consistent with a pathophysiology of T cell clonal expansion. We used diffusion mapping, which localizes datapoints to their eigen components in low-dimesional space, to characterize sources contributing to the gene expression phenotype: the first component was mainly from T cell activation and the second was associated with TCR expression. In LGL the T cell transcriptome appeared to be shaped by both lineage development and TCR rearrangement.Conclusion. We describe at the single cell level T clonal expansion profiles in T-LGL, pre- and post-treatment. Single cell analysis allows accurate recovery of paired α and β chains in the same cell and demonstrates a continuum of cell lineage differentiation. We found a range of differences in transcriptome and TCR repertoires across patients. Transcriptome data, coupled with detailed TCR-based lineage information, provides a rich resource for understanding of the pathology of T-LGL and has implications for prognosis, treatment, and monitoring in the clinic. [Display omitted] DisclosuresYoung:GlaxoSmithKline: Research Funding; CRADA with Novartis: Research Funding; National Institute of Health: Research Funding.