Technical aspects and recommendations for single-cell qPCR

A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing

Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.

Polysaccharides and Structural Proteins as Components in Three-Dimensional Scaffolds for Breast Cancer Tissue Models: A Review

Breast cancer is the most common cancer among women, and even though treatments are available, efficiency varies with the patients. In vitro 2D models are commonly used to develop new treatments. However, 2D models overestimate drug efficiency, which increases the failure rate in later phase III clinical trials. New model systems that allow extensive and efficient drug screening are thus required. Three-dimensional printed hydrogels containing active components for cancer cell growth are interesting candidates for the preparation of next generation cancer cell models. Macromolecules, obtained from marine- and land-based resources, can form biopolymers (polysaccharides such as alginate, chitosan, hyaluronic acid, and cellulose) and bioactive components (structural proteins such as collagen, gelatin, and silk fibroin) in hydrogels with adequate physical properties in terms of porosity, rheology, and mechanical strength. Hence, in this study attention is given to biofabrication methods and to the modification with biological macromolecules to become bioactive and, thus, optimize 3D printed structures that better mimic the cancer cell microenvironment. Ink formulations combining polysaccharides for tuning the mechanical properties and bioactive polymers for controlling cell adhesion is key to optimizing the growth of the cancer cells.

GAGAM v1.2: An Improvement on Peak Labeling and Genomic Annotated Gene Activity Matrix Construction

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology for assessing the epigenetic landscape of thousands of cells. However, the sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. Foremost among them is the peak calling, which interprets the raw scATAC-seq data generating the peaks as features. However, scATAC-seq data are not trivially comparable with single-cell RNA sequencing (scRNA-seq) data, an increasingly pressing challenge since the necessity of multimodal experiments integration. For this reason, this study wants to improve the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing an improved version of the Genomic-Annotated Gene Activity Matrix (GAGAM) concept. Specifically, this paper presents GAGAM v1.2, a new and better version of GAGAM v1.0. GAGAM aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features in scATAC-seq datasets makes different datasets comparable and allows linking gene accessibility and expression. This link is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs and shows a preliminary comparison with scRNA-seq data.

Comparison between relative and absolute quantitative real-time PCR applied to single-cell analyses: Transcriptional levels in a key neuron for long-term memory in the pond snail

Quantitative real-time PCR (qPCR) is a powerful method for measuring nucleic acid levels and quantifying mRNA levels, even in single cells. In the present study, we compared the results of single-cell qPCR obtained by different quantification methods (relative and absolute) and different reverse transcription methods. In the experiments, we focused on the cerebral giant cell (CGC), a key neuron required for the acquisition of conditioned taste aversion in the pond snail Lymnaea stagnalis, and examined changes in the mRNA levels of 3 memory-related genes, cAMP-response element binding proteins (LymCREB1 and LymCREB2) and CREB-binding protein (LymCBP), during memory formation. The results obtained by relative quantification showed similar patterns for the 3 genes. For absolute quantification, reverse transcription was performed using 2 different methods: a mixture of oligo d(T) primers and random primers (RT method 1); and gene-specific primers (RT method 2). These methods yielded different results and did not show consistent changes related to conditioning. The mRNA levels in the samples prepared by RT method 2 were up to 3.3 times higher than those in samples prepared by RT method 1. These results suggest that for qPCR of single neurons, the efficacy and validity do not differ between relative and absolute quantification methods, but the reverse transcription step critically influences the results of mRNA quantification.

Identification of key drivers of the microbial shikimic acid pathway during different materials composting

Metabolites of shikimic acid (SA) pathway can be used as humic substance (HS) precursors. Due to the complexity of SA anabolism, there were few studies on SA pathway during composting. The aim of this study was to identify the key drivers of SA pathway during different materials composting. During composting, the SA, protocatechuic acid (PA) and gallic acid (GA) decreased by 57.09%, 72.27% and 54.04% on average, respectively. The structural equation model showed that SA had key driving factors (organic matter and pH) during lawn waste composting. In addition, the complexity of material structure was the main factor affecting PA driving factors. The factors and degree of influence on GA varied with different materials. Accordingly, this study provided theoretical support for the improvement of SA metabolic intensity by single material and mixed material composting, and further provided a new direction for future HS research.

NANOG initiates epiblast fate through the coordination of pluripotency genes expression

The epiblast is the source of all mammalian embryonic tissues and of pluripotent embryonic stem cells. It differentiates alongside the primitive endoderm in a “salt and pepper” pattern from inner cell mass (ICM) progenitors during the preimplantation stages through the activity of NANOG, GATA6 and the FGF pathway. When and how epiblast lineage specification is initiated is still unclear. Here, we show that the coordinated expression of pluripotency markers defines epiblast identity. Conversely, ICM progenitor cells display random cell-to-cell variability in expression of various pluripotency markers, remarkably dissimilar from the epiblast signature and independently from NANOG, GATA6 and FGF activities. Coordination of pluripotency markers expression fails in Nanog and Gata6 double KO (DKO) embryos. Collectively, our data suggest that NANOG triggers epiblast specification by ensuring the coordinated expression of pluripotency markers in a subset of cells, implying a stochastic mechanism. These features are likely conserved, as suggested by analysis of human embryos.

Pluripotent epiblast cells segregate from primitive endoderm in the blastocyst inner cell mass (ICM). Here the authors show that mosaic epiblast differentiation during mouse and human preimplantation development initiates stochastically in ICM progenitors, independently of the FGF pathway, and requires NANOG activity

Analysis of cf-mtDNA and cf-nDNA fragment size distribution using different isolation methods in BV-2 cell supernatant of starvation-induced autophagy

Fragment size distribution, the important biological properties of cell-free DNA (cfDNA), provides useful information required for diagnostic assay development. However, besides methodological discrepancies, it varies due to the complicated origins and occurrences of in vivo cfDNA. In addition, limited data are available concerning the cfDNA associated with autophagy and distributional difference between cf-mitochondrial DNA (cf-mtDNA) and cf-nuclear DNA (cf-nDNA) fragments. Here we developed an in vitro model of mouse microglial cell (BV-2) with starvation-induced autophagy, in which cfDNA was isolated from the cell supernatant by ultrafiltration (UF) and column-based commercial kit (CC), respectively. Using Agilent 2100 Bioanalyzer, a DNA ladder pattern as the presence of peaks corresponding to mono-, di- and tri-nucleosomes was clearly visualized both in isolation products of UF and CC. However, we also detected shorter fragments than mono-nucleosome by UF. In comparing the UF and CC, we found that the former produced the higher recovery efficiency for spiked-in DNA of shorter fragments than mono-nucleosome in both water and medium, but the latter was superior for spiked-in DNA fragments which were longer than or equal to mono-nucleosome in medium. Combined with these two isolation methods, we have observed that autophagy-associated cf-mtDNA and cf-nDNA were both highly enriched in <mono-nucleosomes fragments more than 71%, and showed no significant differences in the relative percentages for these four fragment sizes. These results have improved our understanding of the fragment size distribution of autophagy-derived cf-mtDNA and cf-nDNA in vitro, and might further develop application of cfDNA as a diagnostic tool.

Tutorial: Guidelines for Single-Cell RT-qPCR

Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.

Opening up the Single-Cell Toolbox for Microbial Natural Products Research

The diverse microbes that produce natural products represent an important source of novel therapeutics, drug leads, and scientific tools. However, the vast majority have not been grown in axenic culture and are members of complex communities. While meta-'omic methods such as metagenomics, -transcriptomics, and -proteomics reveal collective molecular features of this "microbial dark matter", the study of individual microbiome members can be challenging. To address these limits, a number of techniques with single-bacterial resolution have been developed in the last decade and a half. While several of these are embraced by microbial ecologists, there has been less use by researchers interested in mining microbes for natural products. In this review, we discuss the available and emerging techniques for targeted single-cell analysis with a particular focus on applications to the discovery and study of natural products.

The Chimeric Antigen Receptor Detection Toolkit

Chimeric antigen receptor-T (CAR-T) cell therapy is a promising frontier of immunoengineering and cancer immunotherapy. Methods that detect, quantify, track, and visualize the CAR, have catalyzed the rapid advancement of CAR-T cell therapy from preclinical models to clinical adoption. For instance, CAR-staining/labeling agents have enabled flow cytometry analysis, imaging applications, cell sorting, and high-dimensional clinical profiling. Molecular assays, such as quantitative polymerase chain reaction, integration site analysis, and RNA-sequencing, have characterized CAR transduction, expression, and in vivo CAR-T cell expansion kinetics. In vitro visualization methods, including confocal and total internal reflection fluorescence microscopy, have captured the molecular details underlying CAR immunological synapse formation, signaling, and cytotoxicity. In vivo tracking methods, including two-photon microscopy, bioluminescence imaging, and positron emission tomography scanning, have monitored CAR-T cell biodistribution across blood, tissue, and tumor. Here, we review the plethora of CAR detection methods, which can operate at the genomic, transcriptomic, proteomic, and organismal levels. For each method, we discuss: (1) what it measures; (2) how it works; (3) its scientific and clinical importance; (4) relevant examples of its use; (5) specific protocols for CAR detection; and (6) its strengths and weaknesses. Finally, we consider current scientific and clinical needs in order to provide future perspectives for improved CAR detection.

FTIR spectra signatures reveal different cellular effects of EGFR inhibitors on nonsmall cell lung cancer cells

ATP-analogue inhibitors, Gefitinib (Iressa) and Erlotinib (Tarceva) had been approved for advanced and metastatic nonsmall cell lung cancer (NSCLC) cells against tyrosine kinase domain of epidermal growth factor receptor (EGFR). Many techniques have been developed to better understand the drug mechanism which is multistep, time-consuming and expensive. Herein, we performed Fourier-transform infrared (FTIR) microscopy for evaluating the biochemical change on NSCLC (A549) cells after treatment. At levels that produced equivalent effects, Gefitinib dramatically induced cell apoptosis via impaired mitochondrial transmembrane potential. Whereas, Erlotinib had a slight effect on A549. Principal component analysis was performed to distinguish the effect of EGFR inhibitors on A549. FTIR spectra regions were divided into three regions: lipids (3000-2800 cm^-1 ), proteins (1700-1500 cm^-1 ) and carbohydrates and nuclei acids (1200-1000 cm^-1 ). Biochemical changes can be evaluated by these spectral regions. This work may be a novel concept for utilizing FTIR spectroscopy for high-throughput discriminative effects of a drug or compound and its derivatives on cells.

Single-Cell RNA Sequencing of the Cardiovascular System: New Looks for Old Diseases

Cardiovascular disease encompasses a wide range of conditions, resulting in the highest number of deaths worldwide. The underlying pathologies surrounding cardiovascular disease include a vast and complicated network of both cellular and molecular mechanisms. Unique phenotypic alterations in specific cell types, visualized as varying RNA expression-levels (both coding and non-coding), have been identified as crucial factors in the pathology underlying conditions such as heart failure and atherosclerosis. Recent advances in single-cell RNA sequencing (scRNA-seq) have elucidated a new realm of cell subpopulations and transcriptional variations that are associated with normal and pathological physiology in a wide variety of diseases. This breakthrough in the phenotypical understanding of our cells has brought novel insight into cardiovascular basic science. scRNA-seq allows for separation of widely distinct cell subpopulations which were, until recently, simply averaged together with bulk-tissue RNA-seq. scRNA-seq has been used to identify novel cell types in the heart and vasculature that could be implicated in a variety of disease pathologies. Furthermore, scRNA-seq has been able to identify significant heterogeneity of phenotypes within individual cell subtype populations. The ability to characterize single cells based on transcriptional phenotypes allows researchers the ability to map development of cells and identify changes in specific subpopulations due to diseases at a very high throughput. This review looks at recent scRNA-seq studies of various aspects of the cardiovascular system and discusses their potential value to our understanding of the cardiovascular system and pathology.

Technological Advances in Multiscale Analysis of Single Cells in Biomedicine

Single-cell analysis has recently received significant attention in biomedicine. With the advances in super-resolution microscopy, fluorescence labeling, and nanoscale biosensing, new information may be obtained for the design of cancer diagnosis and therapeutic interventions. The discovery of cellular heterogeneity further stresses the importance of single-cell analysis to improve our understanding of disease mechanism and to develop new strategies for disease treatment. To this end, many studies are exploited at the single-cell level for high throughput, highly parallel, and quantitative analysis. Technically, microfluidics are also designed to facilitate single-cell isolation and enrichment for downstream detection and manipulation in a robust, sensitive, and automated manner. Further achievements are made possible by consolidating optically label-free, electrical, and molecular sensing techniques. Moreover, these technologies are coupled with computing algorithms for high throughput and automated quantitative analysis with a short turnaround time. To reflect on how the technological developments have advanced single-cell analysis, this mini-review is aimed to offer readers an introduction to single-cell analysis with a brief historical development and the recent progresses that have enabled multiscale analysis of single-cells in the last decade. The challenges and future trends are also discussed with the view to inspire forthcoming technical developments.

Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method to breast cancer DCCs of a patient undergoing anti-HER2-directed therapy. Here, we were able to measure ERBB2 expression levels in all HER2-protein-positive DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs.

Preamplification with dUTP and Cod UNG Enables Elimination of Contaminating Amplicons

Analyzing rare DNA and RNA molecules in limited sample sizes, such as liquid biopsies and single cells, often requires preamplification, which makes downstream analyses particularly sensitive to polymerase chain reaction (PCR) generated contamination. Herein, we assessed the feasibility of performing Cod uracil-DNA N-glycosylase (Cod UNG) treatment in combination with targeted preamplification, using deoxyuridine triphosphate (dUTP) to eliminate carry-over DNA. Cod UNG can be completely and irreversibly heat inactivated, a prerequisite in preamplification methods, where any loss of amplicons is detrimental to subsequent quantification. Using 96 target assays and quantitative real-time PCR, we show that replacement of deoxythymidine triphosphate (dTTP) with dUTP in the preamplification reaction mix results in comparable dynamic range, reproducibility, and sensitivity. Moreover, Cod UNG essentially removes all uracil-containing template of most assays, regardless of initial concentration, without affecting downstream analyses. Finally, we demonstrate that the use of Cod UNG and dUTP in targeted preamplification can easily be included in the workflow for single-cell gene expression profiling. In summary, Cod UNG treatment in combination with targeted preamplification using dUTP provides a simple and efficient solution to eliminate carry-over contamination and the generation of false positives and inaccurate quantification.

The use of single-cell RNA-Seq to understand virus-host interactions

Single-cell analyses allow uncovering cellular heterogeneity, not only per se, but also in response to viral infection. Similarly, single cell transcriptome analyses (scRNA-Seq) can highlight specific signatures, identifying cell subsets with particular phenotypes, which are relevant in the understanding of virus-host interactions.