A microfluidic approach to parallelized transcriptional profiling of single cells

Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.

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.

An Integrated Preprocessing Approach for Exploring Single-Cell Gene Expression in Rare Cells

Exploring the variability in gene expressions of rare cells at the single-cell level is critical for understanding mechanisms of differentiation in tissue function and development as well as for disease diagnostics and cancer treatment. Such studies, however, have been hindered by major difficulties in tracking the identity of individual cells. We present an approach that combines single-cell picking, lysing, reverse transcription and digital polymerase chain reaction to enable the isolation, tracking and gene expression analysis of rare cells. The approach utilizes a photocleavage bead-based microfluidic device to synthesize and deliver stable cDNA for downstream gene expression analysis, thereby allowing chip-based integration of multiple reactions and facilitating the minimization of sample loss or contamination. The utility of the approach was demonstrated with QuantStudio digital PCR by analyzing the radiation and bystander effect on individual IMR90 human lung fibroblasts. Expression levels of the Cyclin-dependent kinase inhibitor 1a (CDKN1A), Growth/differentiation factor 15 (GDF15), and Prostaglandin-endoperoxide synthase 2 (PTGS2) genes, previously shown to have different responses to direct and bystander irradiation, were measured across individual control, microbeam-irradiated or bystander IMR90 cells. In addition to the confirmation of accurate tracking of cell treatments through the system and efficient analysis of single-cell responses, the results enable comparison of activation levels of different genes and provide insight into signaling pathways within individual cells.

Laser microdissection: A powerful tool for genomics at cell level

Laser microdissection (LM) has become widely democratized over the last fifteen years. Instruments have evolved to offer more powerful and efficient lasers as well as new options for sample collection and preparation. Technological evolutions have also focused on the post-microdissection analysis capabilities, opening up investigations in all disciplines of experimental and clinical biology, thanks to the advent of new high-throughput methods of genome analysis, including RNAseq and proteomics, now globally known as microgenomics, i.e. analysis of biomolecules at the cell level. In spite of the advances these rapidly developing methods have allowed, the workflow for sampling and collection by LM remains a critical step in insuring sample integrity in terms of histology (accurate cell identification) and biochemistry (reliable analyzes of biomolecules). In this review, we describe the sample processing as well as the strengths and limiting factors of LM applied to the specific selection of one or more cells of interest from a heterogeneous tissue. We will see how the latest developments in protocols and methods have made LM a powerful and sometimes essential tool for genomic and proteomic analyzes of tiny amounts of biomolecules extracted from few cells isolated from a complex tissue, in their physiological context, thus offering new opportunities for understanding fundamental physiological and/or patho-physiological processes.

A microchip for integrated single-cell genotoxicity assay

With the development of large-scale biologic databases, precision medicine is becoming a frontier in biomedical research. As a main focus of precision medicine study, cancer has been widely accepted as a disease born out of inherited genetic variations or accumulating genomic damage. At the single-cell level, microfluidics or lab-on-a-chip technology for cancer study is an emerging tool for improving risk assessment, diagnostic categories and therapeutic strategies. This work presents a multi-layer microchip for single-cell gene expression profiling. Treated by three drug reagents (i.e. methyl methanesulfonate, docetaxel and colchicine) with varied concentrations and time lengths, individual human breast cancer cells (MCF-7) are then lysed on-chip, and the released mRNA templates are captured and reversely transcribed into cDNA on microbead surface. Three genes (GAPDH, CDKN1A, AURKA) are amplified and quantified simultaneously through triplex real-time polymerase chain reactions (qPCR). Readout per run is set to be eighteen, and can be further improved following same approach. The microchip is able to integrate all steps of single-cell gene expression profiling, and provide precision study of drug induced genotoxicity with reduced reagents consumption per reaction and instrumental cost.

A Microchip for Integrated Single-Cell Gene Expression Profiling and Genotoxicity Detection

Microfluidics-based single-cell study is an emerging approach in personalized treatment or precision medicine studies. Single-cell gene expression holds a potential to provide treatment selections with maximized efficacy to help cancer patients based on a genetic understanding of their disease. This work presents a multi-layer microchip for single-cell multiplexed gene expression profiling and genotoxicity detection. Treated by three drug reagents (i.e., methyl methanesulfonate, docetaxel and colchicine) with varied concentrations and time lengths, individual human cancer cells (MDA-MB-231) are lysed on-chip, and the released mRNA templates are captured and reversely transcribed into single strand DNA. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), cyclin-dependent kinase inhibitor 1A (CDKN1A), and aurora kinase A (AURKA) genes from single cells are amplified and real-time quantified through multiplex polymerase chain reaction. The microchip is capable of integrating all steps of single-cell multiplexed gene expression profiling, and providing precision detection of drug induced genotoxic stress. Throughput has been set to be 18, and can be further increased following the same approach. Numerical simulation of on-chip single cell trapping and heat transfer has been employed to evaluate the chip design and operation.