High-Throughput Multimodal Single-Cell Targeted DNA and Surface Protein Analysis Using the Mission Bio Tapestri Platform

Beyond single cells: microfluidics empowering multiomics analysis

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

Progress in single-cell multimodal sequencing and multi-omics data integration

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.

Single-cell analysis of the epitranscriptome: RNA modifications under the microscope

The identification of mechanisms capable of modifying genetic information by the addition of covalent RNA modifications distinguishes a level of complexity in gene expression which challenges key long-standing concepts of RNA biology. One of the current challenges of molecular biology is to properly understand the molecular functions of these RNA modifications, with more than 170 different ones having been identified so far. However, it has not been possible to map specific RNA modifications at a single-cell resolution until very recently. This review will highlight the technological advances in single-cell methodologies aimed at assessing and testing the biological function of certain RNA modifications, focusing on m6A. These advances have allowed for the development of novel strategies that enable the study of the 'epitranscriptome'. Nevertheless, despite all these improvements, many challenges and difficulties still need fixing for these techniques to work efficiently.

Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.

High risk for obstructive sleep apnea and risk of hypertension in military personnel: The CHIEF sleep study

BACKGROUND

Epidemiological studies have revealed an association between obstructive sleep apnea (OSA) and hypertension in the general population, while the association in military personnel was rarely investigated.

AIM

To examine the association between high risk for OSA and hypertension by phenotypes in military young adults.

METHODS

A total of 746 military personnel, aged 27.9 years, were included in the cardiorespiratory fitness and health in armed forces (CHIEF)-sleep study in Taiwan in 2020. Antihypertensive medications were not used by the subjects. High risk for OSA was assessed using the Berlin Questionnaire. Hypertension was defined using the 7th Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) and the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) guidelines. The cutoff levels of systolic and diastolic blood pressure (SBP and DBP) for the 2017 ACC/AHA- and JNC 7-based guidelines were 130/140 mmHg and 80/90 mmHg, respectively. Hypertension phenotypes included isolated systolic and diastolic hypertension (ISH, high SBP only and IDH, high DBP only) and combined hypertension (both high SBP and DBP). Multivariable logistic regression analysis with adjustment for demographics, lifestyle and metabolic biomarkers.

RESULTS

The prevalence of high risk for OSA, JNC 7-based hypertension and 2017 ACC/AHA-based hypertension were 8.0%, 5.2% and 22.0%, respectively. Those with a high risk for OSA had a higher probability of JNC 7-based overall and combined hypertension (odds ratios (ORs) and 95% confidence intervals: 2.82 (1.07-7.42) and 7.54 (1.10-51.54), although the probabilities of ISH and IDH were unaffected by a high risk for OSA (ORs: 1.96 and 2.35, respectively, both P > 0.05). In contrast, no associations for any hypertension phenotypes were found according to the 2017 ACC/AHA criteria.

CONCLUSION

A high risk for OSA was associated with severe hypertension and combined hypertension among Asian military young adults.

Partitioning for Easy Multiplexing: A Versatile Droplet PCR Application for Clone Monitoring in Tumors

Clinical genome-wide next-generation sequencing (NGS) has brought new challenges to genetic laboratories. The identification of numerous patient-specific variants that may require to be screened for on multiple other samples poses an issue when striving for time and cost-effectiveness. Here, we propose d-multiSeq, a straightforward method utilizing the advantages of droplet PCR for multiplexing combined with amplicon-based NGS. By comparing d-multiSeq with a standard multiplex amplicon-based NGS, it was shown that partitioning prevents the amplification competition seen when multiplexing and leads to a homogeneous representation of each target in the total read count for up to a 40-target multiplex without the need for prior adjustment. Variant allele frequency was reliably evaluated with a sensitivity of 97.6% for variant allele frequency up to 1%. The applicability of d-multiSeq was also tested on cell-free DNA with the successful amplification of an eight-target multiplex panel. Preliminary application of the technique to assess the clonal evolution in a childhood leukemia harboring high interpatient variability in its somatic variants is shown. d-multiSeq represents a turnkey solution for analyzing large sets of patient-specific variants on low DNA amounts and cell-free DNA.

Recent revelations and future directions using single-cell technologies in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a clinically and biologically heterogeneous disease with varying outcomes. In the last decade, the application of next-generation sequencing technologies has allowed extensive mapping of disease-specific genomic, epigenomic, immunogenetic, and transcriptomic signatures linked to CLL pathogenesis. These technologies have improved our understanding of the impact of tumor heterogeneity and evolution on disease outcome, although they have mostly been performed on bulk preparations of nucleic acids. As a further development, new technologies have emerged in recent years that allow high-resolution mapping at the single-cell level. These include single-cell RNA sequencing for assessment of the transcriptome, both of leukemic and non-malignant cells in the tumor microenvironment; immunogenetic profiling of B and T cell receptor rearrangements; single-cell sequencing methods for investigation of methylation and chromatin accessibility across the genome; and targeted single-cell DNA sequencing for analysis of copy-number alterations and single nucleotide variants. In addition, concomitant profiling of cellular subpopulations, based on protein expression, can also be obtained by various antibody-based approaches. In this review, we discuss different single-cell sequencing technologies and how they have been applied so far to study CLL onset and progression, also in response to treatment. This latter aspect is particularly relevant considering that we are moving away from chemoimmunotherapy to targeted therapies, with a potentially distinct impact on clonal dynamics. We also discuss new possibilities, such as integrative multi-omics analysis, as well as inherent limitations of the different single-cell technologies, from sample preparation to data interpretation using available bioinformatic pipelines. Finally, we discuss future directions in this rapidly evolving field.

Phenotypic signatures of immune selection in HIV-1 reservoir cells

Human immunodeficiency virus 1 (HIV-1) reservoir cells persist lifelong despite antiretroviral treatment1,2 but may be vulnerable to host immune responses that could be exploited in strategies to cure HIV-1. Here we used a single-cell, next-generation sequencing approach for the direct ex vivo phenotypic profiling of individual HIV-1-infected memory CD4+ T cells from peripheral blood and lymph nodes of people living with HIV-1 and receiving antiretroviral treatment for approximately 10 years. We demonstrate that in peripheral blood, cells harbouring genome-intact proviruses and large clones of virally infected cells frequently express ensemble signatures of surface markers conferring increased resistance to immune-mediated killing by cytotoxic T and natural killer cells, paired with elevated levels of expression of immune checkpoint markers likely to limit proviral gene transcription; this phenotypic profile might reduce HIV-1 reservoir cell exposure to and killing by cellular host immune responses. Viral reservoir cells harbouring intact HIV-1 from lymph nodes exhibited a phenotypic signature primarily characterized by upregulation of surface markers promoting cell survival, including CD44, CD28, CD127 and the IL-21 receptor. Together, these results suggest compartmentalized phenotypic signatures of immune selection in HIV-1 reservoir cells, implying that only small subsets of infected cells with optimal adaptation to their anatomical immune microenvironment are able to survive during long-term antiretroviral treatment. The identification of phenotypic markers distinguishing viral reservoir cells may inform future approaches for strategies to cure and eradicate HIV-1.

Frontiers in single cell analysis: multimodal technologies and their clinical perspectives

Single cell multimodal analysis is at the frontier of single cell research: it defines the roles and functions of distinct cell types through simultaneous analysis to provide unprecedented insight into cellular processes. Current single cell approaches are rapidly moving toward multimodal characterizations. It replaces one-dimensional single cell analysis, for example by allowing for simultaneous measurement of transcription and post-transcriptional regulation, epigenetic modifications and/or surface protein expression. By providing deeper insights into single cell processes, multimodal single cell analyses paves the way to new understandings in various cellular processes such as cell fate decisions, physiological heterogeneity or genotype-phenotype linkages. At the forefront of this, microfluidics is key for high-throughput single cell analysis. Here, we present an overview of the recent multimodal microfluidic platforms having a potential in biomedical research, with a specific focus on their potential clinical applications.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Microfluidic Compartmentalization Platforms for Single Cell Analysis

Many cellular analytical technologies measure only the average response from a cell population with an assumption that a clonal population is homogenous. The ensemble measurement often masks the difference among individual cells that can lead to misinterpretation. The advent of microfluidic technology has revolutionized single-cell analysis through precise manipulation of liquid and compartmentalizing single cells in small volumes (pico- to nano-liter). Due to its advantages from miniaturization, microfluidic systems offer an array of capabilities to study genomics, transcriptomics, and proteomics of a large number of individual cells. In this regard, microfluidic systems have emerged as a powerful technology to uncover cellular heterogeneity and expand the depth and breadth of single-cell analysis. This review will focus on recent developments of three microfluidic compartmentalization platforms (microvalve, microwell, and microdroplets) that target single-cell analysis spanning from proteomics to genomics. We also compare and contrast these three microfluidic platforms and discuss their respective advantages and disadvantages in single-cell analysis.