Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes.

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data.

SUMMARY: scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g. RNA and ATAC). AVAILABILITY AND IMPLEMENTATION: scFates is released as open-source software under the BSD 3-Clause 'New' License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on GitHub at https://github.com/LouisFaure/scFates/. Code reproduction and tutorials on published datasets are available on GitHub at https://github.com/LouisFaure/scFates_notebooks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cell segmentation in imaging-based spatial transcriptomics.

Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains. We describe a segmentation method, Baysor, that optimizes two-dimensional (2D) or three-dimensional (3D) cell boundaries considering joint likelihood of transcriptional composition and cell morphology. While Baysor can take into account segmentation based on co-stains, it can also perform segmentation based on the detected transcripts alone. To evaluate performance, we extend multiplexed error-robust fluorescence in situ hybridization (MERFISH) to incorporate immunostaining of cell boundaries. Using this and other benchmarks, we show that Baysor segmentation can, in some cases, nearly double the number of cells compared to existing tools while reducing segmentation artifacts. We demonstrate that Baysor performs well on data acquired using five different protocols, making it a useful general tool for analysis of imaging-based spatial transcriptomics.

RNA velocity-current challenges and future perspectives.

RNA velocity has enabled the recovery of directed dynamic information from single-cell transcriptomics by connecting measurements to the underlying kinetics of gene expression. This approach has opened up new ways of studying cellular dynamics. Here, we review the current state of RNA velocity modeling approaches, discuss various examples illustrating limitations and potential pitfalls, and provide guidance on how the ensuing challenges may be addressed. We then outline future directions on how to generalize the concept of RNA velocity to a wider variety of biological systems and modalities.

Population sequencing data reveal a compendium of mutational processes in the human germ line.

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

Dental cell type atlas reveals stem and differentiated cell types in mouse and human teeth.

Understanding cell types and mechanisms of dental growth is essential for reconstruction and engineering of teeth. Therefore, we investigated cellular composition of growing and non-growing mouse and human teeth. As a result, we report an unappreciated cellular complexity of the continuously-growing mouse incisor, which suggests a coherent model of cell dynamics enabling unarrested growth. This model relies on spatially-restricted stem, progenitor and differentiated populations in the epithelial and mesenchymal compartments underlying the coordinated expansion of two major branches of pulpal cells and diverse epithelial subtypes. Further comparisons of human and mouse teeth yield both parallelisms and differences in tissue heterogeneity and highlight the specifics behind growing and non-growing modes. Despite being similar at a coarse level, mouse and human teeth reveal molecular differences and species-specific cell subtypes suggesting possible evolutionary divergence. Overall, here we provide an atlas of human and mouse teeth with a focus on growth and differentiation.

Spatiotemporal structure of cell fate decisions in murine neural crest.

Neural crest cells are embryonic progenitors that generate numerous cell types in vertebrates. With single-cell analysis, we show that mouse trunk neural crest cells become biased toward neuronal lineages when they delaminate from the neural tube, whereas cranial neural crest cells acquire ectomesenchyme potential dependent on activation of the transcription factor Twist1. The choices that neural crest cells make to become sensory, glial, autonomic, or mesenchymal cells can be formalized as a series of sequential binary decisions. Each branch of the decision tree involves initial coactivation of bipotential properties followed by gradual shifts toward commitment. Competing fate programs are coactivated before cells acquire fate-specific phenotypic traits. Determination of a specific fate is achieved by increased synchronization of relevant programs and concurrent repression of competing fate programs.

RNA velocity of single cells.

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity-the time derivative of the gene expression state-can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

Differential processing of small RNAs during endoplasmic reticulum stress.

The accumulation of misfolded proteins in the endoplasmic reticulum (ER) lumen due to the disruption of the homeostatic system of the ER leads to the induction of the ER stress response. Cellular stress-induced pathways globally transform genes expression on both the transcriptional and post-transcriptional levels with small RNA involvement as regulators of the stress response. The modulation of small RNA processing might represent an additional layer of a complex stress response program. However, it is poorly understood. Here, we studied changes in expression and small RNAs processing upon ER stress in Jurkat T-cells. Induced by ER-stress, depletion of miRNAs among small RNA composition was accompanied by a global decrease of 3' mono-adenylated, mono-cytodinylated and a global increase of 3' mono-uridinylated miRNA isoforms. We observed the specific subset of differentially expressed microRNAs, and also the dramatic induction of 32-nt tRNA fragments precisely phased to 5' and 3' ends of tRNA from a subset of tRNA isotypes. The induction of these tRNA fragments was linked to Angiogenin RNase, which mediates translation inhibition. Overall, the global perturbations of the expression and processing of miRNAs and tiRNAs were the most prominent features of small RNA transcriptome changes upon ER stress.

Evolution of Local Mutation Rate and Its Determinants.

Mutation rate varies along the human genome, and part of this variation is explainable by measurable local properties of the DNA molecule. Moreover, mutation rates differ between orthologous genomic regions of different species, but the drivers of this change are unclear. Here, we use data on human divergence from chimpanzee, human rare polymorphism, and human de novo mutations to predict the substitution rate at orthologous regions of non-human mammals. We show that the local mutation rates are very similar between human and apes, implying that their variation has a strong underlying cryptic component not explainable by the known genomic features. Mutation rates become progressively less similar in more distant species, and these changes are partially explainable by changes in the local genomic features of orthologous regions, most importantly, in the recombination rate. However, they are much more rapid, implying that the cryptic component underlying the mutation rate is more ephemeral than the known genomic features. These findings shed light on the determinants of mutation rate evolution. Key words: local mutation rate, molecular evolution, recombination rate.

APOBEC-induced mutations in human cancers are strongly enriched on the lagging DNA strand during replication.

APOBEC3A and APOBEC3B, cytidine deaminases of the APOBEC family, are among the main factors causing mutations in human cancers. APOBEC deaminates cytosines in single-stranded DNA (ssDNA). A fraction of the APOBEC-induced mutations occur as clusters ("kataegis") in single-stranded DNA produced during repair of double-stranded breaks (DSBs). However, the properties of the remaining 87% of nonclustered APOBEC-induced mutations, the source and the genomic distribution of the ssDNA where they occur, are largely unknown. By analyzing genomic and exomic cancer databases, we show that >33% of dispersed APOBEC-induced mutations occur on the lagging strand during DNA replication, thus unraveling the major source of ssDNA targeted by APOBEC in cancer. Although methylated cytosine is generally more mutation-prone than nonmethylated cytosine, we report that methylation reduces the rate of APOBEC-induced mutations by a factor of roughly two. Finally, we show that in cancers with extensive APOBEC-induced mutagenesis, there is almost no increase in mutation rates in late replicating regions (contrary to other cancers). Because late-replicating regions are depleted in exons, this results in a 1.3-fold higher fraction of mutations residing within exons in such cancers. This study provides novel insight into the APOBEC-induced mutagenesis and describes the peculiarity of the mutational processes in cancers with the signature of APOBEC-induced mutations.

Probing-directed identification of novel structured RNAs.

Transcripts often harbor RNA elements, which regulate cell processes co- or post-transcriptionally. The functions of many regulatory RNA elements depend on their structure, thus it is important to determine the structure as well as to scan genomes for structured elements. State of the art ab initio approaches to predict structured RNAs rely on DNA sequence analysis. They use 2 major types of information inferred from a sequence: thermodynamic stability of an RNA structure and evolutionary footprints of base-pair interactions. In recent years, chemical probing of RNA has arisen as an alternative source of structural information. RNA probing experiments detect positions accessible to specific types of chemicals or enzymes indicating their propensity to be in a paired or unpaired state. There exist several strategies to integrate probing data into RNA secondary structure prediction algorithms that substantially improve the prediction quality. However, whether and how probing data could contribute to detection of structured RNAs remains an open question. We previously developed the energy-based approach RNASurface to detect locally optimal structured RNA elements. Here, we integrate probing data into the RNASurface energy model using a general framework. We show that the use of experimental data allows for better discrimination of ncRNAs from other transcripts. Application of RNASurface to genome-wide analysis of the human transcriptome with PARS data identifies previously undetectable segments, with evidence of functionality for some of them.

Polymerase ζ Activity Is Linked to Replication Timing in Humans: Evidence from Mutational Signatures.

Replication timing is an important determinant of germline mutation patterns, with a higher rate of point mutations in late replicating regions. Mechanisms underlying this association remain elusive. One of the suggested explanations is the activity of error-prone DNA polymerases in late-replicating regions. Polymerase zeta (pol ζ), an essential error-prone polymerase biased toward transversions, also has a tendency to produce dinucleotide mutations (DNMs), complex mutational events that simultaneously affect two adjacent nucleotides. Experimental studies have shown that pol ζ is strongly biased toward GC→AA/TT DNMs. Using primate divergence data, we show that the GC→AA/TT pol ζ mutational signature is the most frequent among DNMs, and its rate exceeds the mean rate of other DNM types by a factor of approximately 10. Unlike the overall rate of DNMs, the pol ζ signature drastically increases with the replication time in the human genome. Finally, the pol ζ signature is enriched in transcribed regions, and there is a strong prevalence of GC→TT over GC→AA DNMs on the nontemplate strand, indicating association with transcription. A recurrently occurring GC→TT DNM in HRAS and SOD1 genes causes the Costello syndrome and amyotrophic lateral sclerosis correspondently; we observe an approximately 1 kb long mutation hotspot enriched by transversions near these DNMs in both cases, suggesting a link between these diseases and pol ζ activity. This study uncovers the genomic preferences of pol ζ, shedding light on a novel cause of mutational heterogeneity along the genome.

RNASurface: fast and accurate detection of locally optimal potentially structured RNA segments.

MOTIVATION: During the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts. RESULTS: In this article, we present an algorithm RNASurface to efficiently scan genomes by constructing a matrix of significance of RNA secondary structures and to identify all locally optimal structured RNA segments up to a predefined size. RNASurface significantly improves precision of identification of known ncRNA in Bacillus subtilis. AVAILABILITY AND IMPLEMENTATION: RNASurface C source code is available from http://bioinf.fbb.msu.ru/RNASurface/downloads.html.