Data-driven detection of age-related arbitrary monotonic changes in single-cell gene expression distributions

Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features.

Integrative genomic analyses reveal putative cell type-specific targets of the Drosophila ets transcription factor Pointed

The Ets domain transcription factors direct diverse biological processes throughout all metazoans and are implicated in development as well as in tumor initiation, progression and metastasis. The Drosophila Ets transcription factor Pointed (Pnt) is the downstream effector of the Epidermal growth factor receptor (Egfr) pathway and is required for cell cycle progression, specification, and differentiation of most cell types in the larval eye disc. Despite its critical role in development, very few targets of Pnt have been reported previously. Here, we employed an integrated approach by combining genome-wide single cell and bulk data to identify putative cell type-specific Pnt targets. First, we used chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) to determine the genome-wide occupancy of Pnt in late larval eye discs. We identified enriched regions that mapped to an average of 6,941 genes, the vast majority of which are novel putative Pnt targets. Next, we integrated ChIP-seq data with two other larval eye single cell genomics datasets (scRNA-seq and snATAC-seq) to reveal 157 putative cell type-specific Pnt targets that may help mediate unique cell type responses upon Egfr-induced differentiation. Finally, our integrated data also predicts cell type-specific functional enhancers that were not reported previously. Together, our study provides a greatly expanded list of putative cell type-specific Pnt targets in the eye and is a resource for future studies that will allow mechanistic insights into complex developmental processes regulated by Egfr signaling.

Ultra-sensitive CTC-based liquid biopsy for pancreatic cancer enabled by large blood volume analysis

The limited sensitivity of circulating tumor cell (CTC) detection in pancreatic adenocarcinoma (PDAC) stems from their extremely low concentration in the whole circulating blood, necessitating enhanced detection methodologies. This study sought to amplify assay-sensitivity by employing diagnostic leukapheresis (DLA) to screen large blood volumes. Sixty patients were subjected to DLA, with a median processed blood volume of ~ 2.8 L and approximately 5% of the resulting DLA-product analyzed using CellSearch (CS). Notably, DLA significantly increased CS-CTC detection to 44% in M0-patients and 74% in M1-patients, yielding a 60-fold increase in CS-CTC enumeration. DLA also provided sufficient CS-CTCs for genomic profiling, thereby delivering additional genomic information compared to tissue biopsy samples. DLA CS-CTCs exhibited a pronounced negative prognostic impact on overall survival (OS), evidenced by a reduction in OS from 28.6 to 8.5 months (univariate: p = 0.002; multivariable: p = 0.043). Additionally, a marked enhancement in sensitivity was achieved (by around 3-4-times) compared to peripheral blood (PB) samples, with positive predictive values for OS being preserved at around 90%. Prognostic relevance of CS-CTCs in PDAC was further validated in PB-samples from 228 PDAC patients, consolidating the established association between CTC-presence and reduced OS (8.5 vs. 19.0 months, p < 0.001). In conclusion, DLA-derived CS-CTCs may serve as a viable tool for identifying high-risk PDAC-patients and aiding the optimization of multimodal treatment strategies. Moreover, DLA enables comprehensive diagnostic profiling by providing ample CTC material, reinforcing its utility as a reliable liquid-biopsy approach. This high-volume liquid-biopsy strategy presents a potential pathway for enhancing clinical management in this malignancy.

Targeted Cell Labeling and Sorting of Prokaryotes for Cultivation and Omics Approaches

To date, the vast majority of prokaryotic organisms escapes detailed characterization because they cannot be isolated in axenic cultures. These organisms are referred to as microbial dark matter. Targeted labelling and sorting of these microorganisms pave the way for single-cell, enrichment, or cultivation approaches. In this review, we describe an array of different methods ranging from labeling-free to specific labelling techniques. In addition, different cell sorting methods and their combinations with targeting strategies are summarized and downstream applications like sequencing and cultivation are reviewed. Recent advances, challenges, and limitations of the particular methods are discussed with respect to cell viability, genome integrity as well as throughput, in order to help researchers select the most suitable methods for their specific research questions.

Bringing precision oncology to cellular resolution with single-cell genomics

Single-cell sequencing technologies have undergone rapid development and adoption by the scientific community in the past 5 years, fueling discoveries about the etiology, pathogenesis, and treatment responsiveness of individual tumor cells within cancer ecosystems. Most of the advancements in our understanding of cancer with these new technologies have focused on basic tumor biology. However, the knowledge produced by these and other studies are beginning to provide biomarkers and drug targets for clinically-relevant subpopulations within a tumor, creating opportunities for the development of biologically-informed, clone-specific combination treatment strategies. Here we provide an overview of the development of the field of single-cell cancer sequencing and provide a roadmap for shepherding these technologies from research tools to diagnostic instruments that provide high-resolution, treatment-directing details of tumors to clinical oncologists.

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 characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities

BACKGROUND

Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention.

METHODS

We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion.

RESULTS

We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq.

CONCLUSIONS

Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.

An efficient single-cell transcriptomics workflow for microbial eukaryotes benchmarked on Giardia intestinalis cells

Background

Most diversity in the eukaryotic tree of life is represented by microbial eukaryotes, which is a polyphyletic group also referred to as protists. Among the protists, currently sequenced genomes and transcriptomes give a biased view of the actual diversity. This biased view is partly caused by the scientific community, which has prioritized certain microbes of biomedical and agricultural importance. Additionally, some protists remain difficult to maintain in cultures, which further influences what has been studied. It is now possible to bypass the time-consuming process of cultivation and directly analyze the gene content of single protist cells. Single-cell genomics was used in the first experiments where individual protists cells were genomically explored. Unfortunately, single-cell genomics for protists is often associated with low genome recovery and the assembly process can be complicated because of repetitive intergenic regions. Sequencing repetitive sequences can be avoided if single-cell transcriptomics is used, which only targets the part of the genome that is transcribed.

Results

In this study we test different modifications of Smart-seq2, a single-cell RNA sequencing protocol originally developed for mammalian cells, to establish a robust and more cost-efficient workflow for protists. The diplomonad Giardia intestinalis was used in all experiments and the available genome for this species allowed us to benchmark our results. We could observe increased transcript recovery when freeze-thaw cycles were added as an extra step to the Smart-seq2 protocol. Further we reduced the reaction volume and purified the amplified cDNA with alternative beads to test different cost-reducing changes of Smart-seq2. Neither improved the procedure, and reducing the volumes by half led to significantly fewer genes detected. We also added a 5′ biotin modification to our primers and reduced the concentration of oligo-dT, to potentially reduce generation of artifacts. Except adding freeze-thaw cycles and reducing the volume, no other modifications lead to a significant change in gene detection. Therefore, we suggest adding freeze-thaw cycles to Smart-seq2 when working with protists and further consider our other modification described to improve cost and time-efficiency.

Conclusions

The presented single-cell RNA sequencing workflow represents an efficient method to explore the diversity and cell biology of individual protist cells.

Untangling early embryo development using single cell genomics

The zygote undergoes five cell divisions prior to the first signs of lineage segregation. Blastocyst formation requires segregation of the trophectoderm, needed for implantation, and the inner cell mass, which differentiate towards the major lineages of the fetus. This process is broadly conserved in mammals, however, in recent years investigations using high throughput single cell transcriptomics have provided new insights on the gene regulatory networks and epigenetic mechanisms controlling these processes in different species, highlighting novel unique evolutionary adaptations. Although analysis of single cell datasets is inherently challenging due to stochastic gene expression in single cells, continuous development of novel computational tools have contributed to improving the quality of these datasets. Single cell -omics provides detailed information on discrete cellular states, and when combined with spatial transcriptomics it can inform on the relationship between cellular compartments and fate determination. This technology has recently been used to shed new light into the progression of lineage segregation, establishment of pluripotency, epigenetic regulation and signalling pathways participating in mammalian pre-gastrulation development. The adoption of these new technologies for generating high-resolution maps of embryogenesis will readily translate into biotechnological applications that will have significant impact in livestock production.

Genome-scale phylogenetics reveals a monophyletic Zoopagales (Zoopagomycota, Fungi)

Previous genome-scale phylogenetic analyses of Fungi have under sampled taxa from Zoopagales; this order contains many predacious or parasitic genera, and most have never been grown in pure culture. We sequenced the genomes of 4 zoopagalean taxa that are predators of amoebae, nematodes, or rotifers and the genome of one taxon that is a parasite of amoebae using single cell sequencing methods with whole genome amplification. Each genome was a metagenome, which was assembled and binned using multiple techniques to identify the target genomes. We inferred phylogenies with both super matrix and coalescent approaches using 192 conserved proteins mined from the target genomes and performed ancestral state reconstructions to determine the ancestral trophic lifestyle of the clade. Our results indicate that Zoopagales is monophyletic. Ancestral state reconstructions provide moderate support for mycoparasitism being the ancestral state of the clade.

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data

BACKGROUND

A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data.

RESULTS

Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other.

CONCLUSIONS

This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.

Single cell genomics to study DNA and chromosome changes in human gametes and embryos

Genomic and chromosomal changes occur with a high rate in the germline and preimplantation embryos. To study such changes directly in the germline of mammals requires access to material as well as single cell genomics. Recent improvements in embryology and single-cell DNA amplification make it possible to study the genomic changes directly in human oocytes, sperm, and preimplantation embryos. This is particularly important for the study of chromosome segregation directly in human oocytes and preimplantation embryos. Here, we present a practical approach how to obtain high quality DNA sequences and genotypes from single cells, using manual handling of the material that makes it possible to detect genomic changes in meiosis and mitosis spanning the entire range from single nucleotide changes to whole chromosome aneuploidies.

Tumor evolution: Linear, branching, neutral or punctuated?

Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Cryptosporidium as a testbed for single cell genome characterization of unicellular eukaryotes

Background

Infectious disease involving multiple genetically distinct populations of pathogens is frequently concurrent, but difficult to detect or describe with current routine methodology. Cryptosporidium sp. is a widespread gastrointestinal protozoan of global significance in both animals and humans.

It cannot be easily maintained in culture and infections of multiple strains have been reported.

To explore the potential use of single cell genomics methodology for revealing genome-level variation in clinical samples from Cryptosporidium-infected hosts, we sorted individual oocysts for subsequent genome amplification and full-genome sequencing.

Results

Cells were identified with fluorescent antibodies with an 80 % success rate for the entire single cell genomics workflow, demonstrating that the methodology can be applied directly to purified fecal samples. Ten amplified genomes from sorted single cells were selected for genome sequencing and compared both to the original population and a reference genome in order to evaluate the accuracy and performance of the method. Single cell genome coverage was on average 81 % even with a moderate sequencing effort and by combining the 10 single cell genomes, the full genome was accounted for. By a comparison to the original sample, biological variation could be distinguished and separated from noise introduced in the amplification.

Conclusions

As a proof of principle, we have demonstrated the power of applying single cell genomics to dissect infectious disease caused by closely related parasite species or subtypes. The workflow can easily be expanded and adapted to target other protozoans, and potential applications include mapping genome-encoded traits, virulence, pathogenicity, host specificity and resistance at the level of cells as truly meaningful biological units.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2815-y) contains supplementary material, which is available to authorized users.

Ecological and evolutionary significance of novel protist lineages

Environmental molecular surveys targeting protist diversity have unveiled novel and uncultured lineages in a variety of ecosystems, ranging from completely new high-rank lineages, to new taxa moderately related to previously described organisms. The ecological roles of some of these novel taxa have been studied, showing that in certain habitats they may be responsible for critical environmental processes. Moreover, from an evolutionary perspective they still need to be included in a more accurate and wider understanding of the eukaryotic tree of life. These seminal discoveries promoted the development and use of a wide range of more in-depth culture-independent approaches to access this diversity, from metabarcoding and metagenomics to single cell genomics and FISH. Nonetheless, culturing using classical or innovative approaches is also essential to better characterize this new diversity. Ecologists and evolutionary biologists now face the challenge of apprehending the significance of this new diversity within the eukaryotic tree of life.

Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases

Single cell genomics has made increasingly significant contributions to our understanding of the role that somatic genome variations play in human neuronal diversity and brain diseases. Studying intercellular genome and epigenome variations has provided new clues to the delineation of molecular mechanisms that regulate development, function and plasticity of the human central nervous system (CNS). It has been shown that changes of genomic content and epigenetic profiling at single cell level are involved in the pathogenesis of neuropsychiatric diseases (schizophrenia, mental retardation (intellectual/leaning disability), autism, Alzheimer's disease etc.). Additionally, several brain diseases were found to be associated with genome and chromosome instability (copy number variations, aneuploidy) variably affecting cell populations of the human CNS. The present review focuses on the latest advances of single cell genomics, which have led to a better understanding of molecular mechanisms of neuronal diversity and neuropsychiatric diseases, in the light of dynamically developing fields of systems biology and "omics".