Single-cell Detection Research Articles

A Review on Nano/Microfluidic Devices for Cell Isolation Techniques: Recent Progress and Advances

Abstract: Micro/nanofluidic devices and systems have gained increasing interest in healthcare applications over the last few decades because of their low cost and ease of customization, with only a small volume of sample fluid required. Many biological queries are now being addressed using various types of single-molecule research. With this rapid rise, the disadvantages of these methods are also becoming obvious. Micro/nanofluidics-based biochemical analysis outperforms traditional approaches in terms of sample volume, turnaround time, ease of operation, and processing efficiency. A complex and multifunctional micro/nanofluidic platform may be used for single-cell manipulation, treatment, detection, and sequencing. We present an overview of the current advances in micro/nanofluidic technology for single-cell research, focusing on cell capture, treatment, and biochemical analyses. The promise of single-cell analysis using micro/nanofluidics is also highlighted.

Single-cell and spatial detection of senescent cells using DeepScence.

Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Single-Cell Detection of Somatic Copy Number Alterations in Human Kidney by Chromatin Accessibility Profiling

Single-Cell Detection of Somatic Copy Number Alterations in Human Kidney by Chromatin Accessibility Profiling

Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease.

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

Microfluidic-Based Electrical Operation and Measurement Methods in Single-Cell Analysis.

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed.

Near‐Infrared Spectroscopy and Aquaphotomics in Cancer Research: A Pilot Study

ABSTRACTCurrently, the majority of methods to monitor cancer treatment through the analysis of body fluids are based on a highly selective detection of single molecules or cells. In this study, we are considering the analysis of the aqueous medium of liquid samples, that is, water, itself, using aquaphotomics and near‐infrared spectroscopy (NIR) for spectral data acquisition and processing, within cancer research. Water, as a molecular system, is a rich source of information about the current state of a patient, which can be extracted from near‐infrared spectra of liquid samples via simple algorithms based on multivariate data analysis. The reported results, obtained ex vivo of body fluids, demonstrate the potential of aquaphotomics in cancer research.

Single-cell detection of copy number changes reveals dynamic mechanisms of adaptation to antifungals in Candida albicans.

Genomic copy number changes are associated with antifungal drug resistance and virulence across diverse fungal pathogens, but the rate and dynamics of these genomic changes in the presence of antifungal drugs are unknown. Here we optimized a dual-fluorescent reporter system in the diploid pathogen Candida albicans to quantify haplotype-specific copy number variation (CNV) and loss of heterozygosity (LOH) at the single-cell level with flow cytometry. We followed the frequency and dynamics of CNV and LOH at two distinct genomic locations in the presence and absence of antifungal drugs in vitro and in a murine model of candidiasis. Copy number changes were rapid and dynamic during adaptation to fluconazole and frequently involved competing subpopulations with distinct genotypes. This study provides quantitative evidence for the rapid speed at which diverse genotypes arise and undergo dynamic population-level fluctuations during adaptation to antifungal drugs in vitro and in vivo.

Single-cell AI-based detection and prognostic and predictive value of DNA mismatch repair deficiency in colorectal cancer

Testing for DNA mismatch repair deficiency (MMRd) is recommended for all colorectal cancers (CRCs). Automating this would enable precision medicine, particularly if providing information on etiology not captured by deep learning (DL) methods. We present AIMMeR, an AI-based method for determination of mismatch repair (MMR) protein expression at a single-cell level in routine pathology samples. AIMMeR shows an area under the receiver-operator curve (AUROC) of 0.98, and specificity of ≥75% at 98% sensitivity against pathologist ground truth in stage II/III in two trial cohorts, with positive predictive value of ≥98% for the commonest pattern of somatic MMRd. Lower agreement with microsatellite instability (MSI) testing (AUROC 0.86) reflects discordance between MMR and MSI PCR rather than AIMMeR misclassification. Analysis of the SCOT trial confirms MMRd prognostic value in oxaliplatin-treated patients; while MMRd does not predict differential benefit of chemotherapy duration, it correlates with difference in relapse by regimen (PInteraction= 0.04). AIMMeR may help reduce pathologist workload and streamline diagnostics in CRC.

Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BackgroundEffective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.ResultsWe present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl.ConclusionsThe REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

ACE-ing Protein Detection in Single Cells

ACE-ing Protein Detection in Single Cells

Recent advances in the design of SERS substrates and sensing systems for (bio)sensing applications: Systems from single cell to single molecule detection

The Raman effect originates from spontaneous inelastic scattering of photons by matter. These photons provide a characteristic fingerprint of this matter, and are extensively utilized for chemical and biological sensing. The probability of generation, and hence the detection of these Raman scattered photons, is very low; hence, it is difficult to use this directly for sensing in complex matrices. To amplify this signal, surface-enhanced Raman spectroscopy (SERS) has been extensively investigated and has emerged as a powerful analytical tool for sensing diverse analytes, including ions, small molecules, inorganics, organics, radionucleotides, and cells. Plasmonic nanoparticles, called hotspots, exhibit localized surface plasmon resonance (LSPR). This amplifies the Raman signal and may offer up to a 1010-fold SERS signal enhancement. The development of SERS active substrates requires further consideration and optimization of several critical features such as surface periodicity, hotspot density, mitigation of sample or surface autofluorescence, tuning of surface hydrophilicities, use of specific (bio) recognition elements with suitable linkers and bioconjugation chemistries, and use of appropriate optics to obtain relevant sensing outcomes in terms of sensitivity, cross-sensitivity, limit of detection, signal-to-noise ratio (SNR), stability, shelf-life, and disposability. This article details the optimization of the aforementioned considerations in the use of disposable materials such as commercial grades of paper, textiles, glasses, polymers, and some specific substrates such as blue-ray digital versatile discs (DVDs) for use as SERS-active substrates for point-of-use (POU) sensing applications. The advancements in these technologies have been reviewed and critiqued for analyte detection in resource-limited settings, highlighting the prospects of applications ranging from single-molecule to single-cell detection.

PACRAT: pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription.

The SARS-CoV-2 pandemic underscored the need for early, rapid, and widespread pathogen detection tests that are readily accessible. Many existing rapid isothermal detection methods use the recombinase polymerase amplification (RPA), which exhibits polymerase chain reaction (PCR)-like sensitivity, specificity, and even higher speed. However, coupling RPA to other enzymatic reactions has proven difficult. For the first time, we demonstrate that with tuning of buffer conditions and optimization of reagent concentrations, RPA can be cascaded into an in vitro transcription reaction, enabling detection using fluorescent aptamers in a one-pot reaction. We show that this reaction, which we term PACRAT (pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription) can be used to detect SARS-CoV-2 RNA with single-copy detection limits, Escherichia coli with single-cell detection limits, and 10-min detection times. Further demonstrating the utility of our one-pot, cascaded amplification system, we show PACRAT can be used for multiplexed detection of the pathogens SARS-CoV-2 and E. coli, along with multiplexed detection of two variants of SARS-CoV-2.

Scm6A: A Fast and Low-cost Method for Quantifying m6A Modifications at the Single-cell Level.

It is widely accepted that N6-methyladenosine (m6A) exhibits significant intercellular specificity, which poses challenges for its detection using existing m6A quantitative methods. In this study, we introduced Single-cell m6A Analysis (Scm6A), a machine learning-based approach for single-cell m6A quantification. Scm6A leverages input features derived from the expression levels of m6A trans regulators and cis sequence features, and offers remarkable prediction efficiency and reliability. To further validate the robustness and precision of Scm6A, we applied a winscore-based m6A calculation method to conduct N6-methyladenosine sequencing (m6A-seq) analysis on CD4+ and CD8+ T-cells isolated through magnetic-activated cell sorting (MACS). Subsequently, we employed Scm6A for analysis on the same samples. Notably, the m6A levels calculated by Scm6A exhibited a significant positive correlation with m6A quantified through m6A-seq in different cells isolated by MACS, providing compelling evidence for Scm6A's reliability. Additionally, we performed single-cell level m6A analysis on lung cancer tissues as well as blood samples from the coronavirus disease 2019 (COVID-19) patients, and demonstrated the landscape and regulatory mechanisms of m6A in different T-cell subtypes from these diseases. In summary, our work has yielded a novel, dependable, and accurate method for single-cell m6A detection. We are confident that Scm6A have broad applications in the realm of m6A-related research.

Synthesis and Characterization of Click Chemical Probes for Single-Cell Resolution Detection of Epichaperomes in Neurodegenerative Disorders.

Neurodegenerative disorders, including Alzheimer's disease (AD) and Parkinson's disease (PD), represent debilitating conditions with complex, poorly understood pathologies. Epichaperomes, pathologic protein assemblies nucleated on key chaperones, have emerged as critical players in the molecular dysfunction underlying these disorders. In this study, we introduce the synthesis and characterization of clickable epichaperome probes, PU-TCO, positive control, and PU-NTCO, negative control. Through comprehensive in vitro assays and cell-based investigations, we establish the specificity of the PU-TCO probe for epichaperomes. Furthermore, we demonstrate the efficacy of PU-TCO in detecting epichaperomes in brain tissue with a cellular resolution, underscoring its potential as a valuable tool for dissecting single-cell responses in neurodegenerative diseases. This clickable probe is therefore poised to address a critical need in the field, offering unprecedented precision and versatility in studying epichaperomes and opening avenues for novel insights into their role in disease pathology.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

Ultra-sensitive and rapid detection of Salmonella enterica and Staphylococcus aureus to single-cell level by aptamer-functionalized carbon nanotube field-effect transistor biosensors

Foodborne diseases caused by Salmonella enterica (S. enterica) and Staphylococcus aureus (S. aureus) significantly impact public health, underscoring the imperative for highly sensitive, rapid, and accurate detection technologies to ensure food safety and prevent human diseases. Nanomaterials hold great promise in the development of high-sensitivity transistor biosensors. In this work, field-effect transistor (FET) comprising high-purity carbon nanotubes (CNTs) were fabricated and modified with corresponding nucleic acid aptamers for the high-affinity and selective capture of S. enterica and S. aureus. The aptamer-functionalized CNT-FET biosensor demonstrated ultra-sensitive and rapid detection of these foodborne pathogens. Experimental results indicated that the biosensor could detect S. enterica at a limit of detection (LOD) as low as 1 CFU in PBS buffer, and S. aureus at an LOD of 1.2 CFUs, achieving single-cell level detection accuracy with exceptional specificity. The biosensor exhibited a rapid response time, completing single detections within 200 s. Even in the presence of interference from six complex food matrices, the biosensor maintained its ultra-sensitive (3.1 CFUs) and rapid response (within 200 s) characteristics for both pathogens. The developed aptamer-functionalized CNT-FET biosensor demonstrates a capability for low-cost, ultra-sensitive, label-free, and rapid detection of low-abundance S. enterica and S. aureus in both buffer solutions and complex environments. This innovation holds significant potential for applying this detection technology to on-site rapid testing scenarios, offering a promising solution to the pressing need for efficient and reliable pathogen monitoring in various settings.

Quantification of MicroRNA in a Single Living Cell via Ionic Current Rectification-Based Nanopore for Triple Negative Breast Cancer Diagnosis.

Accurate analysis of microRNAs (miRNAs) at the single-cell level is extremely important for deeply understanding their multiple and intricate biological functions. Despite some advancements in analyzing single-cell miRNAs, challenges such as intracellular interferences and insufficient detection limits still remain. In this work, an ultrasensitive nanopore sensor for quantitative single-cell miRNA-155 detection is constructed based on ionic current rectification (ICR) coupled with enzyme-free catalytic hairpin assembly (CHA). Benefiting from the enzyme-free CHA amplification strategy, the detection limit of the nanopore sensor for miRNA-155 reaches 10 fM and the nanopore sensor is more adaptable to complex intracellular environments. With the nanopore sensor, the concentration of miRNA-155 in living single cells is quantified to realize the early diagnosis of triple-negative breast cancer (TNBC). Furthermore, the nanopore sensor can be applied in screening anticancer drugs by tracking the expression level of miRNA-155. This work provides an adaptive and universal method for quantitatively analyzing intracellular miRNAs, which will greatly improve our understanding of cell heterogeneity and provide a more reliable scientific basis for exploring major diseases at the single-cell level.

In situ detection of dopamine in single living cells by molecularly imprinted polymer-functionalized nanoelectrodes

In situ detection of dopamine (DA) at single-cell level is critical for exploring neurotransmitter-related biological processes and diseases. However, the low content of DA and a variety of distractors with similar oxidation potentials as DA in cells brought great challenges. Here, a sensitive and specific electrochemical nanosensor was proposed for in situ detection of DA in single living cells based on nanodiamond (ND) and molecularly imprinted polymer (MIP)-functionalized carbon fiber nanoelectrode (ND/MIP/CFNE). Due to its excellent electrocatalytic property, ND was modified to the surface of CFNE based on amide bonding. Compared with bare CFNE, ND-modified CFNE can enhance oxidation currents of DA by about 4-fold, improving signal-to-noise ratio and detection sensitivity. MIP was further electropolymerized on the surface of nanoelectrodes to achieve specific capture and recognition of DA, which could avoid the interference of complex matrix and analogs in cells. Taking advantage of the precise positioning capability of a single-cell analyzer and micromanipulator, ND/MIP/CFNE could be precisely inserted into different locations of single cells and monitor oxidation signal of DA. The concentration of DA in the cytoplasm of single pheochromocytoma (PC12) cell was measured to be about 0.4 μM, providing a sensitive and powerful method for single-cell detection. Furthermore, the nanoelectrodes can monitor the fluctuation of intracellular DA under drug stimulation, providing new ideas and methods for new drug development and efficacy evaluation.