Barcode identification for single cell genomics

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Opportunities to Advance Cervical Cancer Prevention and Care

Cervical cancer (CaCx) is a major public health issue, with over 600,000 women diagnosed annually. CaCx kills someone every 90 seconds, mostly in low- and middle-income countries. There are effective yet imperfect mechanisms to prevent CaCx. Since human papillomavirus (HPV) infections cause most CaCx, they can be prevented by vaccination. Screening methodologies can identify premalignant lesions and allow interventions before a CaCx develops. However, these tools are less feasible in resource-poor environments. Additionally, current screening modalities cannot triage lesions based on their relative risk of progression, which results in overtreatment. CaCx care relies heavily on genotoxic agents that cause severe side effects. This review discusses ways that recent technological advancements could be leveraged to improve CaCx care and prevention.

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

Sequencing the Origins of Life

One goal of origins of life research is to understand how primitive informational and catalytic biopolymers emerged and evolved. Recently, a number of sequencing techniques have been applied to analysis of replicating and evolving primitive biopolymer systems, providing a sequence-specific and high-resolution view of primitive chemical processes. Here, we review application of sequencing techniques to analysis of synthetic and primitive nucleic acids and polypeptides. This includes next-generation sequencing of primitive polymerization and evolution processes, followed by discussion of other novel biochemical techniques that could contribute to sequence analysis of primitive biopolymer driven chemical systems. Further application of sequencing to origins of life research, perhaps as a life detection technology, could provide insight into the origin and evolution of informational and catalytic biopolymers on early Earth or elsewhere.

Single-Cell RNA Sequencing of Bone Marrow Mesenchymal Stem Cells from the Elderly People

Background and Objectives

Bone marrow mesenchymal stem cells (BMSCs) show considerable promise in regenerative medicine. Many studies demonstrated that BMSCs cultured in vitro were highly heterogeneous and composed of diverse cell subpopulations, which may be the basis of their multiple biological characteristics. However, the exact cell subpopulations that make up BMSCs are still unknown.

Methods and Results

In this study, we used single-cell RNA sequencing (scRNA-Seq) to divide 6,514 BMSCs into three clusters. The number and corresponding proportion of cells in clusters 1 to 3 were 3,766 (57.81%), 1,720 (26.40%), and 1,028 (15.78%). The gene expression profile and function of the cells in the same cluster were similar. The vast majority of cells expressed the markers defining BMSCs by flow cytometry and gene expression analysis. Each cluster had at least 20 differentially expressed genes (DEGs). We conducted Gene Ontology enrichment analysis on the top 20 DEGs of each cluster and found that the three clusters had different functions, which were related to self-renewal, multilineage differentiation and cytokine secretion, respectively. In addition, the function of the top 20 DEGs of each cluster was checked by the National Center for Biotechnology Information gene database to further verify our hypothesis.

Conclusions

This study indicated that scRNA-Seq can be used to divide BMSCs into different subpopulations, demonstrating the heterogeneity of BMSCs.

Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering

Despite the scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown "true" clusters. Referencing the transcriptomic heterogeneity of cell clusters, a "true" mRNA number matrix of cell individuals was defined as ground truth. Based on the matrix and the actual data generation procedure, a simulation program (SSCRNA) for raw data was developed. Subsequently, the consistency between simulated data and real data was evaluated. Furthermore, the impact of sequencing depth and algorithms for analyses on cluster accuracy was quantified. As a result, the simulation result was highly consistent with that of the actual data. Among the clustering algorithms, the Gaussian normalization method was the more recommended. As for the clustering algorithms, the K-means clustering method was more stable than K-means plus Louvain clustering. In conclusion, the scRNA simulation algorithm developed restores the actual data generation process, discovers the impact of parameters on classification, compares the normalization/clustering algorithms, and provides novel insight into scRNA analyses.

Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

Efficient CRISPR/Cas9 mediated Pooled-sgRNAs assembly accelerates targeting multiple genes related to male sterility in cotton

BACKGROUND

Upland cotton (Gossypium hirsutum), harboring a complex allotetraploid genome, consists of A and D sub-genomes. Every gene has multiple copies with high sequence similarity that makes genetic, genomic and functional analyses extremely challenging. The recent accessibility of CRISPR/Cas9 tool provides the ability to modify targeted locus efficiently in various complicated plant genomes. However, current cotton transformation method targeting one gene requires a complicated, long and laborious regeneration process. Hence, optimizing strategy that targeting multiple genes is of great value in cotton functional genomics and genetic engineering.

RESULTS

To target multiple genes in a single experiment, 112 plant development-related genes were knocked out via optimized CRISPR/Cas9 system. We optimized the key steps of pooled sgRNAs assembly method by which 116 sgRNAs pooled together into 4 groups (each group consisted of 29 sgRNAs). Each group of sgRNAs was compiled in one PCR reaction which subsequently went through one round of vector construction, transformation, sgRNAs identification and also one round of genetic transformation. Through the genetic transformation mediated Agrobacterium, we successfully generated more than 800 plants. For mutants identification, Next Generation Sequencing technology has been used and results showed that all generated plants were positive and all targeted genes were covered. Interestingly, among all the transgenic plants, 85% harbored a single sgRNA insertion, 9% two insertions, 3% three different sgRNAs insertions, 2.5% mutated sgRNAs. These plants with different targeted sgRNAs exhibited numerous combinations of phenotypes in plant flowering tissues.

CONCLUSION

All targeted genes were successfully edited with high specificity. Our pooled sgRNAs assembly offers a simple, fast and efficient method/strategy to target multiple genes in one time and surely accelerated the study of genes function in cotton.

Low-complexity and highly robust barcodes for error-rich single molecular sequencing

DNA barcodes are frequently corrupted due to insertion, deletion, and substitution errors during DNA synthesis, amplification and sequencing, resulting in index hopping. In this paper, we propose a new DNA barcode construction scheme that combines a cyclic block code with a predetermined pseudo-random sequence bit by bit to form bit pairs, and then converts the bit pairs to bases, i.e., the DNA barcodes. Then, we present a barcode identification scheme for noisy sequencing reads, which uses a combination of cyclic shifting and traditional dynamic programming to mark the insertion and deletion positions, and then performs erasure-and-error-correction decoding on the corrupted codewords. Furthermore, we verify the identification error rate of barcodes for multiple errors and evaluate the reliability of the barcodes in DNA context. This method can be easily generalized for constructing long barcodes, which may be used in scenarios with serious errors. Simulation results show that the bit error rate after identifying insertions/deletions is greatly reduced using the combination of cyclic shift and dynamic programming compared to using dynamic programming only. It indicates that the proposed method can effectively improve the accuracy for estimating insertion/deletion errors. And the overall identification error rate of the proposed method is lower than 10 - 5 when the probability of each base mutation is less than 0.1, which is the typical scenario in third-generation sequencing.

Goals and approaches for each processing step for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data.

Mapping regulators of cell fate determination: Approaches and challenges

Given the limited regenerative capacities of most organs, strategies are needed to efficiently generate large numbers of parenchymal cells capable of integration into the diseased organ. Although it was initially thought that terminally differentiated cells lacked the ability to transdifferentiate, it has since been shown that cellular reprogramming of stromal cells to parenchymal cells through direct lineage conversion holds great potential for the replacement of post-mitotic parenchymal cells lost to disease. To this end, an assortment of genetic, chemical, and mechanical cues have been identified to reprogram cells to different lineages both in vitro and in vivo. However, some key challenges persist that limit broader applications of reprogramming technologies. These include: (1) low reprogramming efficiencies; (2) incomplete functional maturation of derived cells; and (3) difficulty in determining the typically multi-factor combinatorial recipes required for successful transdifferentiation. To improve efficiency by comprehensively identifying factors that regulate cell fate, large scale genetic and chemical screening methods have thus been utilized. Here, we provide an overview of the underlying concept of cell reprogramming as well as the rationale, considerations, and limitations of high throughput screening methods. We next follow with a summary of unique hits that have been identified by high throughput screens to induce reprogramming to various parenchymal lineages. Finally, we discuss future directions of applying this technology toward human disease biology via disease modeling, drug screening, and regenerative medicine.

Rodent models for psychiatric disorders: problems and promises

Psychiatric disorders are a prevalent global health problem, over 900 million individuals affected by a continuum of mental and substance use disorders. Due to this high prevalence, and the substantial direct and indirect societal costs, it is essential to understand the underlying mechanisms of these disorders to facilitate development of new and more effective treatments. Since the advent of recombinant DNA technologies in the early 1980s, genetically modified rodent models have significantly contributed to the genetic and molecular basis of psychiatric disorders. Despite significant advancements, many challenges remain after unsuccessful drug development based on rodent models. Recent human genetics show the polygenetic nature of mental disorders, identifying hundreds of allelic variants that confer increased risk. However, given the complexity of the brain, with many unique cell types, gene expression profiles, and developmental trajectories, proper animal models are needed more than ever to dissect genes and circuits in a cell type-specific manner to advance our understanding and treatment of psychiatric disorders. In this mini-review, we highlight current challenges and promises of using rodent models in advancing science and drug development, focusing on advanced techniques, and their applications to rodent models of psychiatric disorders.

Diversity in biology: definitions, quantification and models

Diversity indices are useful single-number metrics for characterizing a complex distribution of a set of attributes across a population of interest. The utility of these different metrics or sets of metrics depends on the context and application, and whether a predictive mechanistic model exists. In this topical review, we first summarize the relevant mathematical principles underlying heterogeneity in a large population, before outlining the various definitions of 'diversity' and providing examples of scientific topics in which its quantification plays an important role. We then review how diversity has been a ubiquitous concept across multiple fields, including ecology, immunology, cellular barcoding experiments, and socioeconomic studies. Since many of these applications involve sampling of populations, we also review how diversity in small samples is related to the diversity in the entire population. Features that arise in each of these applications are highlighted.

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin's approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads and improves the accuracy of gene abundance estimates. Alevin is considerably faster, typically eight times, than existing gene quantification approaches, while also using less memory.