Single Genomics Research Articles

Printing Microbial Dark Matter: Using Single Cell Dispensing and Genomics to Investigate the Patescibacteria/Candidate Phyla Radiation.

As of today, the majority of environmental microorganisms remain uncultured. They are therefore referred to as “microbial dark matter.” In the recent past, cultivation-independent methods like single-cell genomics (SCG) enabled the discovery of many previously unknown microorganisms, among them the Patescibacteria/Candidate Phyla Radiation (CPR). This approach was shown to be complementary to metagenomics, however, the development of additional and refined sorting techniques beyond the most commonly used fluorescence-activated cell sorting (FACS) is still desirable to enable additional downstream applications. Adding image information on the number and morphology of sorted cells would be beneficial, as would be minimizing cell stress caused by sorting conditions such as staining or pressure. Recently, a novel cell sorting technique has been developed, a microfluidic single-cell dispenser, which assesses the number and morphology of the cell in each droplet by automated light microscopic processing. Here, we report for the first time the successful application of the newly developed single-cell dispensing system for label-free isolation of individual bacteria from a complex sample retrieved from a wastewater treatment plant, demonstrating the potential of this technique for single cell genomics and other alternative downstream applications. Genome recovery success rated above 80% with this technique—out of 880 sorted cells 717 were successfully amplified. For 50.1% of these, analysis of the 16S rRNA gene was feasible and led to the sequencing of 50 sorted cells identified as Patescibacteria/CPR members. Subsequentially, 27 single amplified genomes (SAGs) of 15 novel and distinct Patescibacteria/CPR members, representing yet unseen species, genera and families could be captured and reconstructed. This phylogenetic distinctness of the recovered SAGs from available metagenome-assembled genomes (MAGs) is accompanied by the finding that these lineages—in whole or in part—have not been accessed by genome-resolved metagenomics of the same sample, thereby emphasizing the importance and opportunities of SCGs.

Modular barcode beads for microfluidic single cell genomics

Barcode beads allow efficient nucleic acid tagging in single cell genomics. Current barcode designs, however, are fabricated with a particular application in mind. Repurposing to novel targets, or altering to add additional targets as information is obtained is possible but the result is suboptimal. Here, we describe a modular framework that simplifies generation of multifunctional beads and allows their easy extension to new targets.

Evolutionary stasis of a deep subsurface microbial lineage

Sulfate-reducing bacteria Candidatus Desulforudis audaxviator (CDA) were originally discovered in deep fracture fluids accessed via South African gold mines and have since been found in geographically widespread deep subsurface locations. In order to constrain models for subsurface microbial evolution, we compared CDA genomes from Africa, North America and Eurasia using single cell genomics. Unexpectedly, 126 partial single amplified genomes from the three continents, a complete genome from of an isolate from Eurasia, and metagenome-assembled genomes from Africa and Eurasia shared >99.2% average nucleotide identity, low frequency of SNP’s, and near-perfectly conserved prophages and CRISPRs. Our analyses reject sample cross-contamination, recent natural dispersal, and unusually strong purifying selection as likely explanations for these unexpected results. We therefore conclude that the analyzed CDA populations underwent only minimal evolution since their physical separation, potentially as far back as the breakup of Pangea between 165 and 55 Ma ago. High-fidelity DNA replication and repair mechanisms are the most plausible explanation for the highly conserved genome of CDA. CDA presents a stark contrast to the current model organisms in microbial evolutionary studies, which often develop adaptive traits over far shorter periods of time.

P60.01 Single-Cell Transcriptomics to Assess Response to Immunotherapy in Advanced Lung Cancer ex-vivo: Developing a Functional Predictive Assay

Treatment with immunotherapy (IO) has lead to improvements in survival for patients with advanced lung cancer (ALC) but robust predictive biomarkers remain elusive. Single cell genomics provides an opportunity to study cellular profiles in exceptional detail. Novel transcriptomic (scRNA-seq) including VDJ sequencing, allow evaluation of cellular responses to IO in real-time, providing an opportunity for a predictive test.

Single Cell Physiological Characterization in Living Tissue

The omics approach can provide single cell genomics, proteomic and lipidomics and other biological important parameters and at the level of single cell for some of these methods. However the information obtained is at one time and on a fixed tissue. Even if we had this information it is unclear what we can learn about the interactions of the different compartments in a single cell and even less in a living tissue which involves the interaction among cells. Fluorescence and tissue fluorescence has the potential to offer a comprehensive understanding of the relationship between cellular compartments in the context of a tissue. In this abstract we show new fluorescence technologies that have the potential to reach our goals. To meet this goal, we have constructed a scanning fluorescence microscope that con provide gene expression, proteomics and other parameters in living cell on selected genes and proteins. More important, on the same cells using spectral and lifetime information we can build indexes that correlates with metabolism, with protein interactions, water contentment, molecular crowding, cell to cell contacts and protein aggregation with very high spatial resolution using a STED technique for imaging.

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

Single Cell Genomics for Tumor Heterogeneity.

Single cell genomics became a universal and powerful tool to study cellular diversity at genomic levels in normal and disease conditions. Cancer is a disease of genomic instability which instigates clonal evolution and intra-tumoral heterogeneity. Cancer progression also accompanies gross alterations in the microenvironment, and the stromal or immune cell types comprising the tumor microenvironment can be explored by single cell genomics. So far, breast cancer has been analyzed by single cell genomic tools for the clonal evolution, inter- and intra-tumoral heterogeneity in molecular signatures, and tumor microenvironment. We will briefly go over those studies and discuss the potential application of single cell genomics for the diagnostics and management of cancer.

DiBAC: Distribution-Based Analysis of Cell Differentiation Identifies Mechanisms of Cell Fate

The recent developments in single cell genomics allow for in-depth characterization of cellular heterogeneity in tissue development and the identification of new regulatory mechanisms. Despite these achievements, our understanding of underlying principles in cell fate dynamics is still rather limited. Here, we present a new approach that exploits the high dimensional transcription distributions of single cell RNA sequencing (sc-RNAseq) data by information theorybased measures, which allow for robust identification of cell differentiation properties and efficient differentially expressed gene (DEG) analysis. We show that appropriate binarization of single cell transcription data allows for the rigorous definition of mutual information and robust entropy measures that reflect the general properties of cell fate decisions. We exemplify our distribution-based analysis of cell differentiation (DiBAC) with single cell qPCR data of blood cell development and sc-RNAseq data of Parkinson’s disease-related iPSC differentiation into dopaminergic neurons.

Experimental and bioinformatics considerations in cancer application of single cell genomics

Single cell genomics offers an unprecedented resolution to interrogate genetic heterogeneity in a patient’s tumour at the intercellular level. However, the DNA yield per cell is insufficient for today’s sequencing library preparation protocols. This necessitates DNA amplification which is a key source of experimental noise. We provide an evaluation of two protocols using micro-fluidics based amplification for whole exome sequencing, which is an experimental scenario commonly used in single cell genomics. The results highlight their respective biases and relative strengths in identification of single nucleotide variations. Towards this end, we introduce a workflow SoVaTSiC, which allows for quality evaluation and somatic variant identification of single cell data. As proof of concept, the framework was applied to study a lung adenocarcinoma tumour. The analysis provides insights into tumour phylogeny by identifying key mutational events in lung adenocarcinoma evolution. The consequence of this inference is supported by the histology of the tumour and demonstrates usefulness of the approach.

Single cell genomics for cellular phenotyping: applications in brain disorder

This article explore the avenues opened by single cell genomics in medical research and clinical practice, with focus on brain and neurodevelopmental disorders such as autism.

Single cell genomics for cellular phenotyping: applications in brain disorder

Neurovascular unit (NVU) is an elaborated multicellular brain-vessel-blood interface supporting a controlled blood-brain communication through the selective-permeable blood-brain barrier (BBB) and an adequate neurovascular and neurometabolic coupling. Impairment of NVU during aging and neurologic disorders is accompanied by microvascular dysfunction, BBB opening, neurovascular uncoupling, and neuroinflammation, with deleterious effects on brain microenvironment and neuronal signaling. After stroke, neurons are usually lost in the infarct core and astrocytes become reactive and proliferative, dysregulating the balance between neuronal and non-neuronal cells of the NVU in the lesioned area. In this review, we present major cytological responses of the NVU to cerebral ischemia with an emphasis on the aged brain. Early responses of the neurovascular unit to chronic hypoxia include neutrophils infiltration, brain edema, blood vessel disintegration, astrocytes and endothelial cells proliferation as well as conversion of resident microglia to phagocytic microglia. Later responses include the confinement of the peri-infarcted region by a scar tissue composed mainly of reactive astrocytes and endothelial cells, and angiogenesis. However, the newly formed capillary network is disorganized and the blood vessels are leaky making a successful regeneration of the damaged area, unlikely.

Abstract IA14: Breast cancer evolution – Insights from single cell genomics

Abstract In this talk I will discuss our efforts to develop and apply single cell genome sequencing methods to study premalignant breast cancer invasion, as well as our efforts to build an atlas of normal cell types in human breast tissues. To study invasion in 10 patients with ductal carcinomas in situ (DCIS) premalignancies we developed a Topographic Single Cell Sequencing (TSCS) method that has spatial resolution to measure genomic copy number profiles in patients with synchronous DCIS-IDC tissues. Our data revealed a multi-clonal invasion model, in which genome evolution occurred within the ducts leading to the generation of multiple clones that co-migrated across the basement membrane to establish the invasive carcinomas. Our group is leading a collaborative effort to establish a human breast atlas of normal tissues using single cell RNA sequencing of 30 women. These data have identified major cell types including epithelial cells (luminal HR+, luminal secretory, basal), fibroblasts, adipocytes and endothelial cells (vascular, lymphatic) as well as immune cells (lymphocytes, myeloid). For each cell type, we have identified multiple (2-5) expression states that correspond to different biological functions. Collectively these data provide a ‘human genome’ reference of normal cell types and cell states in the human breast tissues to understand how the stromal and immune microenvironment is reprogrammed in breast cancers. Citation Format: Nicholas E. Navin. Breast cancer evolution – Insights from single cell genomics [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr IA14.

Development and Application of High-Throughput Single Cell Lipid Profiling: A Study of SNCA-A53T Human Dopamine Neurons.

SummaryAdvances in single cell genomics and transcriptomics have shown that at tissue level there is complex cellular heterogeneity. To understand the effect of this inter-cell heterogeneity on metabolism it is essential to develop a single cell lipid profiling approach that allows the measurement of lipids in large numbers of single cells from a population. This will provide a functional readout of cell activity and membrane structure. Using liquid extraction surface analysis coupled with high-resolution mass spectrometry we have developed a high-throughput method for untargeted single cell lipid profiling. This technological advance highlighted the importance of cellular heterogeneity in the functional metabolism of individual human dopamine neurons, suggesting that A53T alpha-synuclein (SNCA) mutant neurons have impaired membrane function. These results demonstrate that this single cell lipid profiling platform can provide robust data that will expand the frontiers in biomedical research.

Single Cell Genomics Reveals Viruses Consumed by Marine Protists.

The predominant model of the role of viruses in the marine trophic web is that of the “viral shunt,” where viral infection funnels a substantial fraction of the microbial primary and secondary production back to the pool of dissolved organic matter. Here, we analyzed the composition of non-eukaryotic DNA associated with individual cells of small, planktonic protists in the Gulf of Maine (GoM) and the Mediterranean Sea. We found viral DNA associated with a substantial fraction cells from the GoM (51%) and the Mediterranean Sea (35%). While Mediterranean SAGs contained a larger proportion of cells containing bacterial sequences (49%), a smaller fraction of cells contained bacterial sequences in the GoM (19%). In GoM cells, nearly identical bacteriophage and ssDNA virus sequences where found across diverse lineages of protists, suggesting many of these viruses are non-infective. The fraction of cells containing viral DNA varied among protistan lineages and reached 100% in Picozoa and Choanozoa. These two groups also contained significantly higher numbers of viral sequences than other identified taxa. We consider mechanisms that may explain the presence of viral DNA in protistan cells and conclude that protistan predation on free viral particles contributed to the observed patterns. These findings confirm prior experiments with protistan isolates and indicate that the viral shunt is complemented by a viral link in the marine microbial food web. This link may constitute a sink of viral particles in the ocean and has implications for the flow of carbon through the microbial food web.

Evaluation of whole genome amplification and bioinformatic methods for the characterization of Leishmania genomes at a single cell level

Here, we report a pilot study paving the way for further single cell genomics studies in Leishmania. First, the performances of two commercially available kits for Whole Genome Amplification (WGA), PicoPLEX and RepliG were compared on small amounts of Leishmania donovani DNA, testing their ability to preserve specific genetic variations, including aneuploidy levels and SNPs. We show here that the choice of WGA method should be determined by the planned downstream genetic analysis, PicoPLEX and RepliG performing better for aneuploidy and SNP calling, respectively. This comparison allowed us to evaluate and optimize corresponding bio-informatic methods. As PicoPLEX was shown to be the preferred method for studying single cell aneuploidy, this method was applied in a second step, on single cells of L. braziliensis, which were sorted by fluorescence activated cell sorting (FACS). Even sequencing depth was achieved in 28 single cells, allowing accurate somy estimation. A dominant karyotype with three aneuploid chromosomes was observed in 25 cells, while two different minor karyotypes were observed in the other cells. Our method thus allowed the detection of aneuploidy mosaicism, and provides a solid basis which can be further refined to concur with higher-throughput single cell genomic methods.

Status and Potential of Single‐Cell Transcriptomics for Understanding Plant Development and Functional Biology

The advent of modern "omics" technologies (genomics, transcriptomics, proteomics, and metabolomics) are attributed to innovative breakthroughs in genome sequencing, bioinformatics, and analytic tools. An organism's biological structure and function is the result of the concerted action of single cells in different tissues. Single cell genomics has emerged as a ground-breaking technology that has greatly enhanced our understanding of the complexity of gene expression at a microscopic resolution and holds the potential to revolutionize the way we characterize complex cell assemblies and study their spatial organization, dynamics, clonal distribution, pathways, function, and networking. Mammalian systems have benefitted immensely from these approaches to dissect complex systems such as cancer, immunological disorders, epigenetic controls of diseases, and understanding of developmental biology. However, the applications of single-cell omics in plant research are just starting. The potential to decipher the fundamentals of developmental and functional biology of large and complex plant species at the single-cell resolution are now becoming important drivers of research. In this review, we present the status, challenges and potential of one important and most commonly used single-cell omics technique in plants, namely single cell transcriptomics. © 2020 International Society for Advancement of Cytometry.

Risk-associated alterations in marrow T cells in pediatric leukemia.

Current management of childhood leukemia is tailored based on disease risk determined by clinical features at presentation. Whether properties of the host immune response impact disease risk and outcome is not known. Here, we combine mass cytometry, single cell genomics, and functional studies to characterize the BM immune environment in children with B cell acute lymphoblastic leukemia and acute myelogenous leukemia at presentation. T cells in leukemia marrow demonstrate evidence of chronic immune activation and exhaustion/dysfunction, with attrition of naive T cells and TCF1+ stem-like memory T cells and accumulation of terminally differentiated effector T cells. Marrow-infiltrating NK cells also exhibit evidence of dysfunction, particularly in myeloid leukemia. Properties of immune cells identified distinct immune phenotype–based clusters correlating with disease risk in acute lymphoblastic leukemia. High-risk immune signatures were associated with expression of stem-like genes on tumor cells. These data provide a comprehensive assessment of the immune landscape of childhood leukemias and identify targets potentially amenable to therapeutic intervention. These studies also suggest that properties of the host response with depletion of naive T cells and accumulation of terminal-effector T cells may contribute to the biologic basis of disease risk. Properties of immune microenvironment identified here may also impact optimal application of immune therapies, including T cell–redirection approaches in childhood leukemia.

Distribution-Free Multisample Tests Based on Optimal Matchings With Applications to Single Cell Genomics

In this article, we propose a nonparametric graphical test based on optimal matching, for assessing the equality of multiple unknown multivariate probability distributions. Our procedure pools the data from the different classes to create a graph based on the minimum non-bipartite matching, and then utilizes the number of edges connecting data points from different classes to examine the closeness between the distributions. The proposed test is exactly distribution-free (the null distribution does not depend on the distribution of the data) and can be efficiently applied to multivariate as well as non-Euclidean data, whenever the inter-point distances are well-defined. We show that the test is universally consistent, and prove a distributional limit theorem for the test statistic under general alternatives. Through simulation studies, we demonstrate its superior performance against other common and well-known multisample tests. The method is applied to single cell transcriptomics data obtained from the peripheral blood, cancer tissue, and tumor-adjacent normal tissue of human subjects with hepatocellular carcinoma and non-small-cell lung cancer. Our method unveils patterns in how biochemical metabolic pathways are altered across immune cells in a cancer setting, depending on the tissue location. All of the methods described herein are implemented in the R package multicross. Supplementary materials for this article are available online.

Abstract 4290: Multi-omic single cell sequencing for deep cell immune profiling and identification of potential biomarkers for cell therapy and immunotherapy

Abstract Introduction: Rapidly advancing single cell genomics technology enables uncovering cell heterogeneity missed by bulk sequencing/analysis. Multi-omic single cell sequencing has ability to assess up to a hundred cell surface proteins, hundreds of transcripts to whole transcriptome, or TCR/BCR gene rearrangement in thousands of cells. Here we report deep cell immune profiling of PBMC by assessing selected cell surface proteins by AbSeq and targeted immune response genes using two single cell sequencing platforms. Experimental procedures: For targeted RNASeq on 10xGenomics Chromium platform, droplets containing single PBMC cell/bead were generated. After cDNA synthesis, libraries with genes of interest were generated using BD Rhapsody Human Immune Response Targeted Panel. For BD Rhapsody multi-omic single cell analysis, PBMC were stained using tagged AbSeq antibodies and sample specific cell tag antibodies. Then viable cells after incubation with cell viability dyes were counted on BD Rhapsody scanner. Some samples were processed for removal of apoptotic cells using EasySep Dead Cell Removal (Annexin V) Kit (Stem Cell Tech). Labeled PBMC from multiple subjects were pooled and loaded to a BD Rhapsody chip. Followed by cell capture beads loading and lysis, mRNA and antibody tags were captured. cDNA was synthesized and libraries containing genes of interest were generated using Human immune response panel along with AbSeq and sample tag libraries. Sequencing was performed on NextSeq 500 (Illumina). Data analysis from 10xGenomics were conduct using its Cell Ranger software. Data analysis from BD Rhapsody workflow were conduct using Seven Bridges Genomics Pipeline and SeqGeq (FlowJo). For comparison, PBMC were also profiled for general major cell populations by flow cytometry. Summary and Conclusions: Targeted RNASeq using both single cell sequencing platforms enables deep cell profiling with benefits of lower cost and higher sensitivity compared with whole transcriptome RNASeq. The results for general cell populations generated by single cell sequencing are comparable to those generated by flow cytometry, however, single cell sequencing classifies sub-populations in greater details based on gene expression markers. Including tagged AbSeq antibodies to assess cell surface proteins in the workflow further helps classification in the case of post-transcriptional regulation, while identifying alternatively spliced isoforms (CD45RO and CD45RA) elusive by RNASeq. One of the challenges is the requirement for viable cells. Although some apoptotic cells could be removed, less viable cells leftover could interfere single cell sequencing and thus careful data QC bioinformatics is warranted. Overall, multi-omic single cell sequencing could enable deep cell profiling and assessing patient samples has great potential to provide biological insights and identify predictive biomarkers. Citation Format: Wenge Shi, Christian Laing, Jane Gao, Kerri Burns, Shyam Sarikonda, Reinhold Pollner, Hua Gong. Multi-omic single cell sequencing for deep cell immune profiling and identification of potential biomarkers for cell therapy and immunotherapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4290.

Targeted Cell Sorting Combined With Single Cell Genomics Captures Low Abundant Microbial Dark Matter With Higher Sensitivity Than Metagenomics.

Rare members of environmental microbial communities are often overlooked and unexplored, primarily due to the lack of techniques capable of acquiring their genomes. Chloroflexi belong to one of the most understudied phyla, even though many of its members are ubiquitous in the environment and some play important roles in biochemical cycles or biotechnological applications. We here used a targeted cell-sorting approach, which enables the selection of specific taxa by fluorescent labeling and is compatible with subsequent single-cell genomics, to enrich for rare Chloroflexi species from a wastewater-treatment plant and obtain their genomes. The combined workflow was able to retrieve a substantially higher number of novel Chloroflexi draft genomes with much greater phylogenetical diversity when compared to a metagenomics approach from the same sample. The method offers an opportunity to access genetic information from rare biosphere members which would have otherwise stayed hidden as microbial dark matter and can therefore serve as an essential complement to cultivation-based, metagenomics, and microbial community-focused research approaches.