scholarly journals Single-Cell Multi-Omics Defines the Cell-Type Specific Impact of SF3B1 Splicing Factor Mutations on Hematopoietic Differentiation in Human Clonal Hematopoiesis and Myelodysplastic Syndromes

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 145-145
Author(s):  
Federico Gaiti ◽  
Allegra Hawkins ◽  
Paulina Chamely ◽  
Ariel Swett ◽  
Xiaoguang Dai ◽  
...  

Abstract Splicing factor mutations are recurrent genetic alterations in blood disorders, highlighting the importance of alternative splicing regulation in hematopoiesis. Specifically, mutations in splicing factor 3B subunit 1 (SF3B1) are implicated in the pathogenesis of myelodysplastic syndromes (MDS) and linked to a high-risk of leukemic transformation in clonal hematopoiesis (CH). SF3B1 mutations are associated with aberrant RNA splicing, leading to increased cryptic 3' splice site (ss) usage and MDS with ring sideroblasts phenotype. The study of mutant SF3B1-driven splicing aberrations in humans has been hampered by the inability to distinguish mutant and wildtype single cells in patient samples and the inadequate coverage of short-read sequencing over splice junctions. To overcome these limitations, we developed GoT-Splice by integrating Genotyping of Transcriptomes (GoT; Nam et al. 2019) with Nanopore long-read single-cell transcriptome profiling and CITE-seq (Fig. A). This allowed for the simultaneous single-cell profiling of protein and gene expression, somatic mutation status, and alternative splicing. Our method selectively enriched full-length sequencing reads with the accurate structure, enabling the capture of higher number of junctions per cell and greater coverage uniformity vs. short-read sequencing (10x Genomics; Fig. B, C). We applied GoT-Splice to CD34+ bone marrow progenitor cells from MDS (n = 15,436 cells across 3 patients; VAF: [0.38-0.4]) to study how SF3B1 mutations corrupt human hematopoiesis (Fig. D). High-resolution mapping of SF3B1 mutvs. SF3B1 wt hematopoietic progenitors revealed an increasing fitness advantage of SF3B1 mut cells towards the megakaryocytic-erythroid lineage, resulting in an expansion of SF3B1 muterythroid progenitor (EP) cells (Fig. E, F). Accordingly, SF3B1 mutEP cells displayed higher protein expression of erythroid lineage markers, CD71 and CD36, vs. SF3B1 wt cells (Fig. G). In these SF3B1 mutEP cells, we identified up-regulation of genes involved in regulation of cell cycle and checkpoint controls (e.g., CCNE1, TP53), and mRNA translation (eIFs gene family; Fig. H). Next, while SF3B1 mut cells showed the expected increase of cryptic 3' splicing vs. SF3B1 wt cells (Fig. I), they exhibited distinct cryptic 3' ss usage as a function of hematopoietic progenitor cell identity, displaying stage-specific aberrant splicing during erythroid maturation (Fig. J). In less differentiated EP cells, we observed mis-splicing of genes involved in iron homeostasis, such as the hypoxia-inducible factor HIF1A, and key regulators of erythroid cell growth, such as SEPT2. At later stages, we observed mis-splicing of BAX, a pro-apoptotic member of the Bcl-2 gene family and transcriptional target of p53, and erythroid-specific genes (e.g., PPOX). We further predicted 54% of the aberrantly spliced mRNAs to introduce premature stop codons, promoting RNA degradation through nonsense-mediated decay (NMD). In line with this notion, we observed a significant decrease in expression of NMD-inducing genes in SF3B1 mut vs . SF3B1 wtEP cells (Fig. K). Lastly, splicing factor mutations observed in CH subjects provide an opportunity to interrogate the downstream impact of SF3B1 mutations prior to development of disease. Like MDS, by applying GoT-splice to CD34+ progenitor cells from SF3B1 mut CH subjects (n = 9,007 cells across 2 subjects; VAF: [0.15-0.22]; Fig. L), we revealed increased mutant cell frequency in EP cells (Fig. M) with concomitant increased expression of genes involved in mRNA translation (Fig. N), consistent with SF3B1 mutation causing mis-splicing injury to translational machinery and ineffective erythropoiesis. Notably, CH patients already exhibited cell-type specific cryptic 3' ss usage in SF3B1 mut cells (Fig. O). In summary, we developed a novel multi-omics single-cell toolkit to examine the impact of splicing factor mutations on cellular fitness directly in human samples. With this approach, we showed that, while SF3B1 mutations arise in uncommitted HSCs, their effect on fitness increases with differentiation into committed EPs, in line with the mutant SF3B1-driven dyserythropoiesis phenotype. We revealed that SF3B1 mutations exert cell-type specific mis-splicing that leads to abnormal erythropoiesis. Finally, we demonstrated that the impact of SF3B1 mutations on EP cells begins before disease onset, as observed in CH subjects. Figure 1 Figure 1. Disclosures Dai: Oxford Nanopore Technologies: Current Employment. Beaulaurier: Oxford Nanopore Technologies: Current Employment. Drong: Oxford Nanopore Technologies: Current Employment. Hickey: Oxford Nanopore Technologies: Current Employment. Juul: Oxford Nanopore Technologies: Current Employment. Wiseman: Astex: Research Funding; Novartis: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy; StemLine: Consultancy. Harrington: Oxford Nanopore Technologies: Current Employment. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy. Abdel-Wahab: H3B Biomedicine: Consultancy, Research Funding; Foundation Medicine Inc: Consultancy; Merck: Consultancy; Prelude Therapeutics: Consultancy; LOXO Oncology: Consultancy, Research Funding; Lilly: Consultancy; AIChemy: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Envisagenics Inc.: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees.

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Yan Zhong ◽  
Guanxun Li ◽  
Qian Xu ◽  
Andrew E. Hillhouse ◽  
...  

Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1832-1832
Author(s):  
Francesca Arruga ◽  
Valeria Bracciamà ◽  
Alison Yeomans ◽  
Annalisa D'Avola ◽  
Marta Coscia ◽  
...  

Abstract BACKGROUND. Mutations in NOTCH1 PEST domain (NOTCH1-M) are present in ~10% of Chronic Lymphocytic Leukemia (CLL) patients, result in accumulation of more stable NOTCH1 protein, and associate with poorer prognosis. NOTCH1-M are enriched in unmutated (U) immunoglobulin gene heavy-chain variable region (IGHV) CLL, which show high surface IgM (sIgM) expression and signaling capacity. mRNA translation is a prominent response to B cell receptor (BCR) engagement, increased in U-CLL, and for which therapeutic inhibitors are under active development. In CLL, c-MYC is an essential mediator of BCR-driven translation and direct target of NOTCH1, suggesting the impact of NOTCH1 on anti-IgM-mediated cell growth via MYC. AIMS AND METHODS. Our aim was to investigate the functional role of NOTCH1-M on anti-IgM-mediated signaling, compared to wild-type (WT) NOTCH1. The impact on global mRNA translation was studied using a flow cytometry-based O-propargyl-puromycin (OPP) incorporation assay and polysome fractionation assays. The effects of stabilized vs WT NOTCH1 were measured after 24-hour cultures of CLL cells, when data demonstrate differences in the expression of the two forms. Two cohorts of U-CLLs were compared: i) a subset of samples carrying NOTCH1-M [variant allele frequency (VAF) ≥30%, n=21] and ii) a cohort of samples with WT NOTCH1 (VAF<1%, n=23). In both subsets no additional cytogenetic lesions other than 13q deletion were present. RESULTS. sIgM levels and signaling capacity (measured by anti-IgM mediated iCa2+ mobilization) were higher in NOTCH1-M than in -WT samples, consistent with previous observations (1). Conceivably, anti-IgM-mediated phosphorylation of PLCg2 and ERK1/2 was stronger in M than in WT CLLs. In keeping with these results, expression of downstream targets as MYC and CCL3 was also induced at higher levels in M samples. Interestingly, inhibition of NOTCH1 with g-secretase inhibitor (DAPT) significantly decreased BCR target genes induction in M cells, reducing the differences with WT samples, and further enhanced the effects of ibrutinib when used in combination. In order to investigate the impact of NOTCH1 on IgM-mediated CLL cell growth, anti-IgM-induced global mRNA translation was compared in the two cohorts. Consistent with the higher MYC mRNA and protein levels, anti-IgM led to higher global mRNA translation in NOTCH1-M than in -WT cells. DAPT inhibited it in both CLL subsets, while ibrutinib led to complete inhibition of mRNA translation only in the -WT subset, suggesting a major contribution of NOTCH1 to the process. Consistently, the combination of DAPT+ibrutinib abrogated the difference between M and WT CLL cells. Importantly, MYC (but not translation initiation factors eIF4G, eIF4A or eIF3b) was already induced at 6 hours following anti-IgM stimulation and was maintained at high levels at 24 hours, while up-regulation of eIF4G, eIF4A and eIF3b was evident only at 24 hours, supporting the hypothesis of a direct MYC-dependent regulation of the translation machinery (2). NOTCH1 itself was post-transcriptionally regulated upon BCR ligation, as we observed increased NOTCH1 mRNA in polysome-enriched actively translated fractions and increased protein levels on the surface of anti-IgM stimulated cells, specifically inhibited by ibrutinib. Consequently, NOTCH1 pathway was significantly more activated upon anti-IgM stimulation in M than WT cells, as determined by qPCR of NOTCH1 target genes. Both Ibrutinib and DAPT significantly prevented NOTCH1 activation upon BCR triggering, with the drug combination being the most effective treatment. Moreover, in line with data showing NOTCH1-dependent regulation of a B cell gene signature, expression of BTK, LYN and BLNK was significantly increased in anti-IgM activated NOTCH1-M samples, an effect prevented by DAPT. CONCLUSIONS. These data indicate that NOTCH1 stabilization associates with stronger IgM signaling capacity and suggest an interplay between BCR and NOTCH1 pathway, with the former promoting NOTCH1 expression and activation. The evidence that NOTCH1 pathway inhibition reverts this difference suggests a direct effect of NOTCH1 on IgM signaling. In this scenario, stabilizing NOTCH1 mutations may enhance BCR signaling by boosting translation through MYC induction and by directly regulating expression of BCR cascade elements. NOTES. SD and FF share senior authorshipD'Avola, Blood 2016Ruggero, Cancer Res 2009 Disclosures Coscia: Abbvie, Gilead, Shire: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen, Karyopharm: Research Funding. Gaidano:Janssen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Morphosys: Honoraria; Amgen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Allan:Genentech: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees; Sunesis: Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy; Verastem: Membership on an entity's Board of Directors or advisory committees. Furman:Gilead: Consultancy; AbbVie: Consultancy; Verastem: Consultancy; Janssen: Consultancy; Genentech: Consultancy; Incyte: Consultancy, Other: DSMB; Loxo Oncology: Consultancy; TG Therapeutics: Consultancy; Sunesis: Consultancy; Acerta: Consultancy, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy. Packham:Aquinox: Research Funding. Deaglio:iTeos therapeutics: Research Funding; VelosBio inc: Research Funding; Verastem: Research Funding. Forconi:Abbvie: Consultancy; Janssen-Cilag: Consultancy.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 37-37
Author(s):  
Franco Castillo Tokumori ◽  
Chetasi Talati ◽  
Najla E. Al Ali ◽  
David A. Sallman ◽  
Seongseok Yun ◽  
...  

CONTEXT: Splicing factor mutations (SRSF2, U2AF1, SF3B1, and ZRSR2) are present in ~50% of myelofibrosis (MF) patients. SRSF2 and U2AF1 Q157 are considered to be high-risk mutations, while the prognostic significance of ZRSR2 and SF3B1 has not been well established. As a group, splicing mutations are associated with cytopenias, the management of which is an area of unmet clinical need in MF. OBJECTIVE: To describe the clinical characteristics, treatment approaches, and outcomes in MF patients with splicing mutations. DESIGN: This is a single-institution, retrospective analysis of 133 MF patients with splicing mutations who presented to our institution between 2006 and 2019. PMF, post-ET MF, and post-PV MF were defined according to the World Health Organization and International Working Group criteria, respectively. Baseline variables were compared between patients harboring different splicing factor mutations and different mutations within the same splicing gene. Median overall survival (OS) was measured from time of diagnosis to date of death or censored at last follow up or date of transplant. Kaplan-Meier plots were created to compare LFS and OS among treatment cohorts, and differences were assessed using Log-rank tests. RESULTS: Among 133 MF patients with a splicing mutation, SRSF2 mutations were most common (n = 48), followed by U2AF1 (n = 36), SF3B1 (n = 27) and ZRSR2 mutations (n = 24). Most SRSF2 mutations occurred at P95 (90%). Thirty (83%) U2AF1 mutations occurred at Q157, with 5 (14%) at S34. Fourteen (63%) SF3B1 mutations occurred K666, with 9 (33%) at K700. Thirteen (54%) ZRSR2 mutations were in-frame insertions/deletions, 4 (17%) frameshift mutations, 3 (13%) nonsense mutations and 4 (17%) missense. All frameshift/nonsense ZRSR2 mutations occurred in males. Spliceosome mutations were mutually exclusive but for 2 cases (one had U2AF1 and SRSF2 mutations and the other had SF3B1 and ZRSR2 mutations). Baseline characteristics were similar between splicing mutations. The presence of a U2AF1 mutation correlated with lower hemoglobin (p 0.018) and U2AF1 Q157 mutations were associated with thrombocytopenia p=0.051) and higher DIPSS-plus scores (p=0.006). Severe thrombocytopenia (platelets &lt; 50 x 109/L) was present in 20 (17%) patients and enriched in those with U2AF1 mutations (n = 9). ASXL1 mutations rarely occurred in conjunction with SF3B1 mutations (p = 0.007). Among all patients with splicing mutations, median OS was 60.6 months. Median OS was decreased in patients with SRSF2 mutations (33 vs 106 months, p=0.001) compared to those with other splicing mutations. Median OS was increased in patients with SF3B1 mutations compared to patients with other splicing mutations (181 mo vs 42 mo, p = 0.002). Median OS for patients with U2AF1 and ZRSR2 mutations was 44 and 106 months, respectively. Among patients with U2AF1 mutations, the presence of severe thrombocytopenia was associated with inferior survival (13.9 mo vs not reached, p = 0.045). The presence of an SRSF2 mutation was associated with an increased risk of leukemic transformation (24% vs 3%, p = 0.002). Among patients with SRSF2 mutations, median OS in those with documented leukemic transformation was 32.9 mo compared to 48.7 mo in those without (p = 0.17). CONCLUSIONS: Splicing mutations in MF have unique phenotypic and prognostic correlations. While SRSF2 mutations appear detrimental, SF3B1 mutations correlate with favorable outcomes. While U2AF1 and SRSF2 mutations are considered high-risk in MF, the impact appears driven by cytopenias in the former and leukemic transformation in the latter. This may hold relevance when considering therapeutic approaches in these patients. Disclosures Talati: AbbVie: Honoraria; Jazz: Speakers Bureau; Astellas: Speakers Bureau; BMS: Honoraria; Pfizer: Honoraria. Sallman:Celgene, Jazz Pharma: Research Funding; Agios, Bristol Myers Squibb, Celyad Oncology, Incyte, Intellia Therapeutics, Kite Pharma, Novartis, Syndax: Consultancy. Sweet:Takeda: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Stemline: Honoraria; Agios: Membership on an entity's Board of Directors or advisory committees; Astellas: Honoraria. Padron:Incyte: Research Funding; Kura: Research Funding; BMS: Research Funding; Novartis: Honoraria. Lancet:Abbvie: Consultancy; Agios Pharmaceuticals: Consultancy, Honoraria; Astellas Pharma: Consultancy; Celgene: Consultancy, Research Funding; Daiichi Sankyo: Consultancy; ElevateBio Management: Consultancy; Jazz Pharmaceuticals: Consultancy; Pfizer: Consultancy. Komrokji:Geron: Honoraria; Novartis: Honoraria; Acceleron: Honoraria; Incyte: Honoraria; Abbvie: Honoraria; Agios: Speakers Bureau; BMS: Honoraria, Speakers Bureau; Jazz: Honoraria, Speakers Bureau. Kuykendall:Blueprint Medicines: Research Funding; BMS: Research Funding; Incyte: Research Funding; Novartis: Research Funding.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 678-678
Author(s):  
Robert M. Myers ◽  
Franco Izzo ◽  
Tamara Prieto ◽  
Eleni Mimitou ◽  
Ramya Raviram ◽  
...  

Abstract In hematopoiesis, changes in chromatin accessibility define priming and commitment of hematopoietic precursors towards cellular fates. In turn, somatic mutations in hematopoietic stem and progenitor cells (HSPCs) drive the onset and progression of myeloid disorders, such as myeloproliferative neoplasms (MPNs), and reshape differentiation topologies. To chart how somatic mutations disrupt the epigenetic landscape in human clonal outgrowths, we developed Genotyping of Targeted loci with Chromatin Accessibility (GoT-ChA), linking genotypes to chromatin accessibility across thousands of single cells. Crucially, GoT-ChA captures genotypes directly from genomic DNA (Fig. 1a), and thus independently of the genomic location or expression of the target. We tested GoT-ChA via cell line mixing studies targeting either TP53 R248Q (Fig. 1b), or JAK2 V617F (Fig. 1c), assigning cell line identity solely based on chromatin accessibility. Notably, GoT-ChA resulted in genotyping of 54% of cells with 96.9% accuracy for TP53 R248Q and 60% of cells with 99.7% accuracy for JAK2 V617F. These data show the transformative advantage of targeting DNA directly, as prior high-throughput droplet methods to target the lowly expressed JAK2 via cDNA resulted in genotyping of only ~7% of cells (Nam et al, Nature, 2019). Next, we applied GoT-ChA to CD34 + cells from JAK2 V617F-mutant myelofibrosis (MF) samples (Fig. 1d). We clustered cells based on chromatin profiles, revealing the expected cell populations in hematopoiesis (Fig. 1d-e), and then projected genotyping status onto the differentiation map (Fig. 1f). In further validation of genotyping accuracy, copy number inference showed a sample that contained a partial deletion of chromosome 20 (Fig. 1g) concordant with our genotyping. Furthermore, GoT-ChA can be integrated with recent protocols to allow for high mitochondrial genome coverage (Lareau et al, Nature Biotechnology, 2020). We observed mitochondrial mutations that were highly concordant with JAK2 V617F (Fig. 1h), allowing genotyping of &gt;85% of cells. Within MPN samples, wildtype (WT) and mutated (MUT) cells were intermingled across the differentiation topology. Nonetheless, we observed an increase in the mutant fraction within erythroid progenitors (EP; Fig. 1i). Moreover, pseudo-temporal ordering of chromatin accessibility revealed that the mutant cell fraction increased along erythroid or megakaryocyte differentiation in untreated MPN (Fig 1j), in line with clinical phenotypes. Chromatin accessibility profiles can provide clues to the underlying regulatory network through transcription factor (TF) motif accessibility. Uniquely, GoT-ChA enables de novo differential motif accessibility, directly comparing WT and MUT cells co-existing within the same bone marrow. Mutant HSPCs showed increased motif accessibility (FDR &lt; 0.05) for TFs associated with erythropoiesis (Fig. 1k), suggestive of increased erythroid priming. Within EP clusters, we observed increased motif accessibility of STAT5A and STAT5B, downstream targets of JAK2 (Fig. 1l-m). These data demonstrated a cell-type specific effect of the JAK2 V617F mutation. Ruxolitinib is a frontline JAK1/2 inhibitor for MF. Despite improvements in quality of life, ruxolitinib does not clearly target the MPN clone or prevent progression of disease. In ruxolitinib-treated patients the MUT cell fraction was uniformly distributed along the differentiation (Fig.1i-j), demonstrating an abrogation of the fitness advantage of JAK2 V617F in committed progenitors, but not in HSPCs. Consistently, STAT5A motif accessibility remained increased in MUT cells at intermediate stages of erythroid maturation but decreased to similar levels as WT cells at later stages (Fig. 1n-o). Overall, GoT-ChA radically expands the single-cell multi-omics toolkit and obviates limiting dependencies on target gene transcription, allowing high throughput somatic genotype-to-phenotype mapping. Applied to JAK2 V617F-mutated MPN, GoT-ChA uncovered a cell-type specific fitness advantage with erythroid commitment, that was reversed upon JAK2 inhibitor treatment. The reshaping of the differentiation topography traced back to differential transcription factor activity driving uncommitted vs. committed JAK2 V617F progenitors. Thus, single-cell multi-omics with GoT-ChA enables to chart the epigenetic underpinnings of hematopoietic clonal outgrowth. Figure 1 Figure 1. Disclosures Mimitou: Immunai: Current Employment. Hoffman: Protagonist Therapeutics, Inc.: Consultancy; Kartos Therapeutics, Inc.: Research Funding; Novartis: Other: Data Safety Monitoring Board, Research Funding; AbbVie Inc.: Other: Data Safety Monitoring Board, Research Funding. Abdel-Wahab: Prelude Therapeutics: Consultancy; LOXO Oncology: Consultancy, Research Funding; Merck: Consultancy; Foundation Medicine Inc: Consultancy; H3B Biomedicine: Consultancy, Research Funding; Lilly: Consultancy; AIChemy: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Envisagenics Inc.: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees. Smibert: Immunai: Current Employment.


Author(s):  
Andrew Farmer ◽  
Sandra Thibivilliers ◽  
Kook Hui Ryu ◽  
John Schiefelbein ◽  
Marc Libault

AbstractSimilar to other complex organisms, plants consist of diverse and highly specialized cell types. The gain of unique biological functions of these different cell types is the consequence of the establishment of cell-type-specific transcriptional programs and their associated regulatory mechanisms. Recently, single cell transcriptomic approaches have been applied on Arabidopsis thaliana root protoplasts allowing the accurate characterization of the transcriptional profiles of the cell-types composing seedling roots. As a first step in gaining a deeper understanding of the regulatory mechanisms controlling Arabidopsis gene expression, we report the use of single nucleus RNA sequencing (sNucRNA-seq) and single nucleus Assay for Transposase Accessible Chromatin sequencing (sNucATAC-seq) technologies on Arabidopsis roots. The comparison of our single nuclei transcriptomes to previously published protoplast transcriptomes validated the use of nuclei as biological entities to establish cell-type specific transcriptomes from multicellular organs. Furthermore, our sNucRNA-seq results uncovered the transcriptome of additional cell subtypes not identified by scRNA-seq. Similar to our transcriptomic approach, the sNucATAC-seq approach led to the distribution of the Arabidopsis nuclei into distinct clusters suggesting the differential remodeling of the chromatin between groups of cells according to their identity. To reveal the impact of chromatin remodeling on gene transcription, we integrated sNucRNA-seq and sNucATAC-seq data and demonstrated that cell-type-specific marker genes also display cell-type-specific pattern of chromatin accessibility. Our data suggest that the differential remodeling of the chromatin is a critical mechanism to regulate gene activity at the cell-type level.


2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i610-i617
Author(s):  
Mohammad Lotfollahi ◽  
Mohsen Naghipourfar ◽  
Fabian J Theis ◽  
F Alexander Wolf

Abstract Motivation While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation. Results We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%. Availability and implementation The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.


PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205883 ◽  
Author(s):  
Joseph C. Mays ◽  
Michael C. Kelly ◽  
Steven L. Coon ◽  
Lynne Holtzclaw ◽  
Martin F. Rath ◽  
...  

2022 ◽  
Author(s):  
Luisa Santus ◽  
Raquel García-Pérez ◽  
Maria Sopena-Rios ◽  
Aaron E Lin ◽  
Gordon C Adams ◽  
...  

Long non-coding RNAs (lncRNAs) are pivotal mediators of systemic immune response to viral infection, yet most studies concerning their expression and functions upon immune stimulation are limited to in vitro bulk cell populations. This strongly constrains our understanding of how lncRNA expression varies at single-cell resolution, and how their cell-type specific immune regulatory roles may differ compared to protein-coding genes. Here, we perform the first in-depth characterization of lncRNA expression variation at single-cell resolution during Ebola virus (EBOV) infection in vivo. Using bulk RNA-sequencing from 119 samples and 12 tissue types, we significantly expand the current macaque lncRNA annotation. We then profile lncRNA expression variation in immune circulating single-cells during EBOV infection and find that lncRNAs' expression in fewer cells is a major differentiating factor from their protein-coding gene counterparts. Upon EBOV infection, lncRNAs present dynamic and mostly cell-type specific changes in their expression profiles especially in monocytes, the main cell type targeted by EBOV. Such changes are associated with gene regulatory modules related to important innate immune responses such as interferon response and purine metabolism. Within infected cells, several lncRNAs have positively and negatively correlated expression with viral load, suggesting that expression of some of these lncRNAs might be directly hijacked by EBOV to attack host cells. This study provides novel insights into the roles that lncRNAs play in the host response to acute viral infection and paves the way for future lncRNA studies at single-cell resolution.


2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.


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