|8:35 AM - 8:40 AM||SCANGEN: Introduction|
|8:40 AM - 9:00 AM||Integrated genetic and transcriptional analysis at the single-cell level||Jean Fan, Harvard University, United States|
|9:00 AM - 9:20 AM||Determing the Mechanism of 5-Azacytidine Response in Myeloid Malignancies Using Single-cell DNA Methylation Sequencing Paired With Flow Cytometry||Kieran O'Neill, The University of British Columbia, BC Cancer, Canada|
|9:20 AM - 9:40 AM||Methods for Identifying Tumor Heterogeneity and Rare Subclones in Single Cell DNA Sequence Data||Sombeet Sahu, MissionBio, United States|
|9:40 AM - 10:15 AM||Coffee Break|
|10:15 AM - 10:20 AM||SCANGEN: Welcome Back|
|10:20 AM - 10:40 AM||Scalable Bayesian Tensor Factorization for single-cell Genomics||Christopher Yau, University of Birmingham and The Alan Turing Institute, United Kingdom|
|10:40 AM - 11:00 AM||STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data||Huidong Chen, Harvard Medical School, United States|
|11:00 AM - 11:20 AM||Leveraging transcription factor networks to identify cell types from single-cell transcriptomes||Sahar Ansari, University of Michigan, United States|
|11:20 AM - 11:40 AM||Granatum X: A community engaging and flexible scRNA-Seq analysis environment connecting tool developers to bench scientists||Xun Zhu, University of Hawaii Cancer Center, United States|
|11:40 AM - 12:00 PM||An accurate and robust imputation method scImpute for single-cell RNA-seq data||Wei Vivian Li, University of California, Los Angeles, United States|
|12:00 PM - 12:20 PM||An approach to studying clonal cell populations using bulk exome and single-cell RNA sequencing data||Davis McCarthy, EMBL-EBI |
|12:20 PM - 12:40 PM||Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations||Simone Ciccolella, Università degli studi Milano Bicocca, Italy|
|12:40 PM - 2:00 PM||Lunch and SCANGEN Posters|
|2:00 PM - 2:20 PM||TBC||Florian Markowetz, CRUK Cambridge Institute, United Kingdom|
|2:20 PM - 2:40 PM||SPhyR: Tumor Phylogeny Estimation from Single-Cell Sequencing Data under Loss and Error||Mohammed El-Kebir, University of Illinois at Urbana-Champaign, United States|
|2:40 PM - 3:00 PM||Single-cell sequencing analysis of pancreatic cancer precursor lesions reveals convergent evolution and polyclonal origins||Violeta Beleva Guthrie, Johns Hopkins University, United States|
|3:00 PM - 3:20 PM||Deconvolution of tumor copy number data using bulk and single-cell sequencing data||Haoyun Lei, Carnegie Mellon University, United States|
|3:20 PM - 3:40 PM||Modelling tumour evolution from single-cell sequencing data||Katharina Jahn, ETH Zurich, Switzerland|
|3:40 PM - 4:00 PM||Regulatory underpinnings of intra-tumor heterogeneity in HCC liver cancer||Bojan Losic, Icahn School of Medicine at Mount Sinai Hospital, United States|
|4:00 PM - 4:20 PM||Investigating Haematopoietic Stem Cells development by single cell sequencing||Sumon Ahmed, The University of Manchester, United Kingdom|
|4:20 PM - 4:40 PM||A multitask learning approach for clustering multiple single cell RNA-seq datasets||Rui Kuang, University of Minnesota Twin Cities, United States|
Myeloid malignancies, especially myelodysplastic syndromes (MDS) are often treated with the DNA methyltransferase inhibitor, 5-azacytidine (5-Aza), but with variable response rates. To determine factors predicting response, we single-cell bisfulite sequenced the stem cell fraction (CD34+CD38-Lin-) of bone marrow from five patients treated with 5-Aza. By examining methylation within DNA binding ChIP-seq footprints, we were able to find DNA binding proteins (especially CTCF and cohesins) that may provide a signature for future diagnostic prediction of 5-Aza response. Functional analysis of these differentially methylated regions showed changes in enhancers and super-enhancers that regulate normal hematopoeisis as well as cell growth and proliferation. Accompanying single-cell immunophenotypic data from index-sorting shows a distinct CD34(bright)CD99(bright)CD45RA- signature for responders. Future work with a larger cohort will help to validate these candidate immunophenotypic and epigenetic predictors of 5-Aza response.
Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited.
Here we present STREAM (Single-cell Trajectories Reconstruction, Exploration And Mapping), an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. STREAM has four innovations compared to other existing methods: 1) a novel density-level trajectory visualization useful to study subpopulation composition and cell-fate genes along branching trajectories, 2) a documented end-to-end pipeline to reconstruct trajectories from chromatin-accessibility data, 3) the first interactive database focused on single-cell trajectory visualization for several published studies ,and 4) a trajectory mapping procedure to readily map new cells to precomputed structures without pooling data and re- computing trajectories. This last innovation allows facile analysis of data from genomic perturbation studies or to assign cancer cells to a normal developmental trajectory. STREAM is available as a user-friendly interactive website at http://stream.pinellolab.org/ .
Cancer stem cells (CSC) are characterized by the dual capacity of either self-renewal or differentiation to other cell types. They are often resistant to anti-cancer treatments and become the cellular source of relapse or metastasis. With the advent of high-throughput technologies such as Drop-seq, which make it possible to capture the expression profiles of thousands of cells, it may be possible to identify rare cell populations like CSCs. In this project, we analyze ten breast cancer cell lines at the single-cell level, seeking to understand the heterogeneity among these cultured cells and whether some cells could resemble CSCs.
We propose to exploit prior knowledge of transcription factors (TF) and their target genes. We leverage these known interactions to estimate the likely gene activity of individual transcription factors by summing the expression levels of its downstream targets. The resulting score, based on the expression of all its target genes, could be a more accurate indicator of activity than the observed expression level of the TF per se. This approach essentially transformed the original data for individual genes into aggregate data for individual TFs. We used this method to identify clusters of cells and found some TF-based profiles across multiple cell lines.
Granatum is an online scRNA-Seq analysis platform offering biologists the access to the latest methods developed in the bioinformatics community, especially for those without programming experience. Its rich feature and friendly interface has earned its recent popularity (over 1,000 unique users within 6 months of publication). Building upon this success and taking in users’ feedback, we here present Granatum X: the next generation scRNA-Seq analysis platform. The new core/plugin architecture of Granatum X allows for easy incorporation of modules developed by the bioinformatics community. The new flexible pipeline structure enables customization of workflows. The new containerization solution of Granatum X with Gboxes allows its deployment on the cloud, HPC, or private servers seamlessly. With these significant design upgrades, we anticipate Granatum X to become a community engaging, flexible, evolving software eco-environment for scRNA-Seq analysis, which connects programming developers and end users.
The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.
In recent years, the well-known Infinite Sites Assumption (ISA) has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions seen as an accumulation of mutations. However, recent studies leveraging Single-cell Sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. Still, established methods that can infer phylogenies with mutation losses are however lacking. We present the SASC (Simulated Annealing Single-Cell inference) tool which is a new and robust approach based on simulated annealing for the inference of cancer progression from SCS data. More precisely, we introduce a simple extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of back mutations in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real data sets and in comparison with some other available methods. The Simulated Annealing Single-cell inference (SASC) tool is open source and available at https://github.com/sciccolella/sasc.
Cancer is characterized by intra-tumor heterogeneity, the presence of distinct cell populations with distinct complements of somatic mutations, which include single-nucleotide variants (SNVs) and copy-number aberrations (CNAs). Single-cell sequencing technology enables one to study these cell populations at the level of individual cells. Phylogeny estimation algorithms that employ appropriate evolutionary models are key to understanding the evolutionary mechanisms behind intra-tumor heterogeneity.
We introduce Single-cell Phylogeny Reconstruction (SPhyR), a method for tumor phylogeny estimation from single-cell sequencing data. In light of frequent loss of SNVs due to CNAs in cancer, SPhyR employs the $k$-Dollo evolutionary model, where a mutation can only be gained once but lost $k$ times. Underlying SPhyR is a combinatorial characterization of solutions as constrained integer matrix completions, based on a new connection to the cladistic multi-state perfect phylogeny problem.
On simulated data, we show that SPhyR outperforms existing methods, that are either based on the infinite sites (SCITE) or the finite sites (SiFit) evolutionary model, in terms of solution quality and run time. On real data, we show that SPhyR provides a likelier explanation of the evolutionary history of a metastatic colorectal cancer. In summary, SPhyR enables detailed evolutionary analyses of single-cell cancer sequencing data.
Intraductal papillary mucinous neoplasms (IPMNs) are precursors to pancreatic cancer, however, little is known about genetic heterogeneity in these lesions. In this study, we identify somatic mutations in single neoplastic cells from fresh IPMN tissue from surgical resections and provide new insights into the clonal structure of these precursor lesions. In each single cell, whole genome amplification, followed by targeted next generation sequencing of pancreatic driver genes, was performed. A novel custom analysis pipeline was developed to determine single-cell genotypes by leveraging information jointly from multiple samples. The pipeline integrates features of current single-cell, somatic mutation, and multi-sample callers. Our analyses revealed that different mutations in the same driver gene frequently occur in unique tumor clones within the same IPMN, suggesting the possibility of polyclonal origin or an unidentified initiating event preceding this critical mutation. Multiple mutations in later-occurring driver genes were also common and frequently localized to unique tumor clones, raising the possibility of convergent evolution of these genetic events in pancreatic tumorigenesis. In conclusion, analysis of single-cell sequencing of IPMNs demonstrated genetic heterogeneity with respect to early and late occurring driver gene mutations, suggesting a more complex pattern of tumor evolution than previously appreciated in these lesions.
As part of an investigation of intra-tumoral heterogeneity(ITH) in HCC liver cancer we initiated a pilot study which multi-regionally sampled 14 patients and harvested 79 tumor biopsies, along with matched adjacent normal samples. Twelve patients were analyzed using bulk omics to obtain readouts of HLA allele specific expression, neo-antigen calling from somatic mutations, and TCR profiling using VDJ sequencing. Immune editing was observed in the neo-antigen landscape with subclonal mutations recruiting the dominant TIL response, which suggestively correlated with clinically observed clonal de-differentiation.
To further investigate these signals and deconvolve impurity and cell-type confounders, we examined the remaining two patients (7 biopsies) with 10X Chromium sc-RNA-seq. We show that constructing HCC-cell only co-expression networks enriched in the binding motif of key transcription factors ('regulons'), and then binarizing the activity of these networks in each cell, reveals that such regulons are remarkably regionally specifically distributed among the cancer cells. This implies that the molecular scale of HCC clonal evolution in terms of downstream expression reprogramming is strongly regionally specific and potentially suggests interesting bounds on the molecular emergence of phenotypic ITH as a function of clonal evolution.
scRNA-seq enables detailed profiling of heterogeneous cell populations to reveal lineage relationships or discover new cell types. Due to lower read coverage/tag counts, often limited number of sampled cells and other dataset specific variations in scRNA-seq data, there has been little success directed towards developing computational methods for cross-dataset analysis of multiple single-cell populations. We introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to three real scRNA-seq/droplet-based datasets, scVDMC more accurately detected cell populations and known cell markers than other recent scRNA-seq clustering methods. In the case study on an in-house Recessive Dystrophic Epidermolysis Bullosa scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry.