Amy Guillaumet-Adkins

Comparative Analysis of Single-Cell RNA Sequencing Methods

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

Dual MET and ERBB inhibition overcomes intratumor plasticity in osimertinib-resistant-advanced non-small-cell lung cancer (NSCLC)

Abstract Background Third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) such as osimertinib are the last line of targeted treatment of metastatic non-small-cell lung cancer (NSCLC) EGFR-mutant harboring T790M. Different mechanisms of acquired resistance to third-generation EGFR-TKIs have been proposed. It is therefore crucial to identify new and effective strategies to overcome successive acquired mechanisms of resistance. Methods For Amplicon-seq analysis, samples from the index patient (primary and metastasis lesions at different timepoints) as well as the patient-derived orthotopic xenograft tumors corresponding to the different treatment arms were used. All samples were formalin-fixed paraffin-embedded, selected and evaluated by a pathologist. For droplet digital PCR, 20 patients diagnosed with NSCLC at baseline or progression to different lines of TKI therapies were selected. Formalin-fixed paraffin-embedded blocks corresponding to either primary tumor or metastasis specimens were used for analysis. For single-cell analysis, orthotopically grown metastases were dissected from the brain of an athymic nu/nu mouse and cryopreserved at −80°C. Results In a brain metastasis lesion from a NSCLC patient presenting an EGFR T790M mutation, we detected MET gene amplification after prolonged treatment with osimertinib. Importantly, the combination of capmatinib (c-MET inhibitor) and afatinib (ErbB-1/2/4 inhibitor) completely suppressed tumor growth in mice orthotopically injected with cells derived from this brain metastasis. In those mice treated with capmatinib or afatinib as monotherapy, we observed the emergence of KRAS G12C clones. Single-cell gene expression analyses also revealed intratumor heterogeneity, indicating the presence of a KRAS-driven subclone. We also detected low-frequent KRAS G12C alleles in patients treated with various EGFR-TKIs. Conclusion Acquired resistance to subsequent EGFR-TKI treatment lines in EGFR-mutant lung cancer patients may induce genetic plasticity. We assess the biological insights of tumor heterogeneity in an osimertinib-resistant tumor with acquired MET-amplification and propose new treatment strategies in this situation.

Bigscale: An analytical framework for big-scale single-cell data

Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.

Evaluation of single-cell genomics to address evolutionary questions using three SAGs of the choanoflagellate Monosiga brevicollis

Single-cell genomics (SCG) appeared as a powerful technique to get genomic information from uncultured organisms. However, SCG techniques suffer from biases at the whole genome amplification step that can lead to extremely variable numbers of genome recovery (5–100%). Thus, it is unclear how useful can SCG be to address evolutionary questions on uncultured microbial eukaryotes. To provide some insights into this, we here analysed 3 single-cell amplified genomes (SAGs) of the choanoflagellate Monosiga brevicollis, whose genome is known. Our results show that each SAG has a different, independent bias, yielding different levels of genome recovery for each cell (6–36%). Genes often appear fragmented and are split into more genes during annotation. Thus, analyses of gene gain and losses, gene architectures, synteny and other genomic features can not be addressed with a single SAG. However, the recovery of phylogenetically-informative protein domains can be up to 55%. This means SAG data can be used to perform accurate phylogenomic analyses. Finally, we also confirm that the co-assembly of several SAGs improves the general genomic recovery. Overall, our data show that, besides important current limitations, SAGs can still provide interesting and novel insights from poorly-known, uncultured organisms.

Single-Cell Genomics Unravels Brain Cell-Type Complexity

The brain is the most complex tissue in terms of cell types that it comprises, to the extent that it is still poorly understood. Single cell genome and transcriptome profiling allow to disentangle the neuronal heterogeneity, enabling the categorization of individual neurons into groups with similar molecular signatures. Herein, we unravel the current state of knowledge in single cell neurogenomics. We describe the molecular understanding of the cellular architecture of the mammalian nervous system in health and in disease; from the discovery of unrecognized cell types to the validation of known ones, applying these state-of-the-art technologies.

matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments

Single-cell transcriptomics allows the identification of cellular types, subtypes and states through cell clustering. In this process, similar cells are grouped before determining co-expressed marker genes for phenotype inference. The performance of computational tools is directly associated to their marker identification accuracy, but the lack of an optimal solution challenges a systematic method comparison. Moreover, phenotypes from different studies are challenging to integrate, due to varying resolution, methodology and experimental design. In this work we introduce matchSCore (https://github.com/elimereu/matchSCore), an approach to match cell populations fast across tools, experiments and technologies. We compared 14 computational methods and evaluated their accuracy in clustering and gene marker identification in simulated data sets. We further used matchSCore to project cell type identities across mouse and human cell atlas projects. Despite originating from different technologies, cell populations could be matched across data sets, allowing the assignment of clusters to reference maps and their annotation.

Comparison of single-cell whole-genome amplification strategies

Single-cell genomics is an alluring area that holds the potential to change the way we understand cell populations. Due to the small amount of DNA within a single cell, whole-genome amplification becomes a mandatory step in many single-cell applications. Unfortunately, single-cell whole-genome amplification (scWGA) strategies suffer from several technical biases that complicate the posterior interpretation of the data. Here we compared the performance of six different scWGA methods (GenomiPhi, REPLIg, TruePrime, Ampli1, MALBAC, and PicoPLEX) after amplifying and low-pass sequencing the complete genome of 230 healthy/tumoral human cells. Overall, REPLIg outperformed competing methods regarding DNA yield, amplicon size, amplification breadth, amplification uniformity –being the only method with a random amplification bias–, and false single-nucleotide variant calls. On the other hand, non-MDA methods, and in particular Ampli1, showed less allelic imbalance and ADO, more reliable copy-number profiles and less chimeric amplicons. While no single scWGA method showed optimal performance for every aspect, they clearly have distinct advantages. Our results provide a convenient guide for selecting a scWGA method depending on the question of interest while revealing relevant weaknesses that should be considered during the analysis and interpretation of single-cell sequencing data.

Single cell expression analysis uncouples transdifferentiation and reprogramming

Many somatic cell types are plastic, having the capacity to convert into other specialized cells (transdifferentiation)(1) or into induced pluripotent stem cells (iPSCs, reprogramming)(2) in response to transcription factor over-expression. To explore what makes a cell plastic and whether these different cell conversion processes are coupled, we exposed bone marrow derived pre-B cells to two different transcription factor overexpression protocols that efficiently convert them either into macrophages or iPSCs and monitored the two processes over time using single cell gene expression analysis. We found that even in these highly efficient cell fate conversion systems, cells differ in both their speed and path of transdifferentiation and reprogramming. This heterogeneity originatesin two starting pre-B cell subpopulations,large pre-BII and the small pre-BII cells they normally differentiate into. The large cells transdifferentiate slowly but exhibit a high efficiency of iPSC reprogramming. In contrast, the small cells transdifferentiate rapidly but are highly resistant to reprogramming. Moreover, the large B cells induce a stronger transient granulocyte/macrophage progenitor (GMP)-like state, while the small B cells undergo a more direct conversion to the macrophage fate. The large cells are cycling and exhibit high Myc activity whereas the small cells are Myc low and mostly quiescent. The observed heterogeneity of the two cell conversion processes can therefore be traced to two closely related cell types in the starting population that exhibit different types of plasticity. These data show that a somatic cell’s propensity for either transdifferentiation and reprogramming can be uncoupled. One sentence summary Single cell transcriptomics of cell conversions