Microfluidics applications for high-throughput single cell sequencing

Abstract

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

Microfluidics for Neuronal Cell and Circuit Engineering

“…Single-cell RNA-Sequencing (scRNA-Seq), for example, allows dissection of the transcriptional profiles of individual brain cells. ,,, Subsequent processing of such transcriptomic data using machine learning algorithms, i.e., Seurat, permit clustering of neurons with similar gene expression profiles . ScRNA-Seq is also useful to validate the identity of stem cell-derived neuronal cells by comparing their gene expression profiles with those of primary neurons. ,− Over the past decade, high-throughput scRNA-Seq data from different brain regions have been used to generate mouse and human neuronal cell atlases. ,− Similarly, genome, transcriptome, and epigenome sequencing assays at consecutive neuronal differentiation time points during embryonic or postnatal development have allowed to elucidate with unprecedented resolution the dynamic molecular changes that neuronal progenitor cells must undergo to differentiate . Together, these data are central to deciphering the molecular mechanisms underlying neuronal diversity across species. ,, …”

“…The design of barcoded beads includes a segment to attach the capturing oligonucleotide to the bead, a primer segment to amplify the captured transcript, a cell barcode that is the same for all oligonucleotides on one bead (to identify all transcripts originating from one particular cell), unique molecular identifiers (UMIs) for digitally counting RNA molecules and correcting amplification artifacts, and a polyd­(T) segment to capture polyadenylated RNA . InDrop performs reverse transcription in droplets, and then cDNA is collected for amplification, while Drop-Seq releases beads from droplets for reverse transcription and then cDNA is amplified by PCR . Meanwhile, in the 10× Genomics platform, cell lysis, and cDNA library preparation occurs immediately after cells are encapsulated in gel bead-in-emulsions (GEMs) .…”

“…Overall, microfluidics have had a major impact on the generation of genomic and transcriptomic data from native brain tissues and organoids (Table ). These data are of great value not only for classifying neuronal cell subtypes based on their transcriptomic profile but also for devising strategies to direct the differentiation of hiPSCs toward specific neuronal cell fates . In this regard, integration of in vivo or in vitro electrophysiological recordings and morphological evaluations combined with scRNA-Seq data of the same cells provides information to precisely map neuronal subtypes and predict their functional contributions in brain networks. ,− Patch-Seq is an example of a low-throughput method capable of linking the transcriptomic profile of neuronal cells to their neurophysiological and morphological phenotypes and can also be used to investigate the cellular response to diverse chemical stimuli. − Notably, while high-throughput automated patch-clamp electrophysiology tools are available since the 1990s and early 2000s, they still need to be integrated with scRNA-Seq platforms .…”

“…iPSC-derived neurons serve as building blocks to form complex neural circuits . Although there are still no protocols for the derivation of many neuronal subtypes, research on using neural stem cells (NSCs) for neural tissue engineering and repair has progressed at a steady pace, with microfluidics supporting this progress by allowing to develop simplified on-a-chip models of brain circuitries. ,− Further, microfluidics have also been a major driver for omics (i.e., genomics, proteomics, transcriptomics), facilitating to extract in-depth molecular data from native brain tissues and organoids . In this context, the increasing number of transcriptomic atlases characterizing the gene expression profiles of cells and/or nuclei from different brain regions represent extremely valuable databases for the field of neuronal cell engineering .…”

Recent Advances in Single-Cell View of Mesenchymal Stem Cell in Osteogenesis

“…The key to scRNA-seq technology is single-cell isolation and independent library construction. Using droplet-based microfluidics technology, single cell and gel beads containing a barcode, unique molecular index, primers, and enzymes were wrapped in an oil droplet through a microfluidic chip ( Zhou et al, 2021 ). The barcode in each oil droplet is a unique DNA sequence, thus allowing the distinction of the source of the target sequence during sequencing ( Figure 2 ).…”