Single-cell Measurements Research Articles

MALDI MSI Protocol for Spatial Bottom-Up Proteomics at Single-Cell Resolution.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) started with spatial mapping of peptides and proteins. Since then, numerous bottom-up protocols have been developed. However, achievable spatial resolution and sample preparation with many wet steps hindered the development of single cell-level workflows for bottom-up spatial proteomics. This study presents a protocol optimized for MALDI-MSI measurements of single cells within the context of their 2D culture. Sublimation of CHCA, followed by a dip in ice-cold ammonium phosphate monobasic (AmP), produced peptide-rich mass spectra while maintaining matrix crystal sizes around 400 nm. This enables MALDI-MSI imaging of proteins in single cells grown on an ITO slide with a throughput of approximately 7800 cells per day. 89 peptide-like features corresponding to a single MDA-MB-231 breast cancer cell were detected. Furthermore, by combining the MALDI-MSI data with LC-MS/MS data obtained on cell pellets, we have successfully identified 24 peptides corresponding to 17 proteins, including actin, vimentin, and transgelin-2.

Automated yeast cultivation control using a biosensor and flow cytometry.

Effective microbial bioprocessing relies on maintaining ideal cultivation conditions, highlighting the necessity for tools that monitor and regulate cellular performance and robustness. This study evaluates a fed-batch cultivation control system based on at-line flow cytometry monitoring of intact yeast cells having a fluorescent transcription factor-based redox biosensor. Specifically, the biosensor assesses the response of an industrial xylose-fermenting Saccharomyces cerevisiae strain carrying the TRX2p-yEGFP biosensor for NADPH/NADP+ ratio imbalance when exposed to furfural. The developed control system successfully detected biosensor output and automatically adjusted furfural feed rate, ensuring physiological fitness at high furfural levels. Moreover, the single-cell measurements enabled the monitoring of subpopulation dynamics, enhancing control precision over traditional methods. The presented automated control system highlights the potential of combining biosensors and flow cytometry for robust microbial cultivations by leveraging intracellular properties as control inputs.

Time-reversal symmetry breaking in the chemosensory array reveals a general mechanism for dissipation-enhanced cooperative sensing

The Escherichia coli chemoreceptors form an extensive array that achieves cooperative and adaptive sensing of extracellular signals. The receptors control the activity of histidine kinase CheA, which drives a nonequilibrium phosphorylation-dephosphorylation reaction cycle for response regulator CheY. Cooperativity and dissipation are both important aspects of chemotaxis signaling, yet their consequences have only been studied separately. Recent single-cell FRET measurements revealed that kinase activity of the array spontaneously switches between active and inactive states, with asymmetric switching times that signify time-reversal symmetry breaking in the underlying dynamics. Here, we present a nonequilibrium lattice model of the chemosensory array, which demonstrates that the observed asymmetric switching dynamics can only be explained by an interplay between the dissipative reactions within individual core units and the cooperative coupling between neighboring units. Microscopically, the switching time asymmetry originates from irreversible transition paths. The model shows that strong dissipation enables sensitive and rapid signaling response by relieving the speed-sensitivity trade-off, which can be tested by future single-cell experiments. Overall, our model provides a general framework for studying biological complexes composed of coupled subunits that are individually driven by dissipative cycles and the rich nonequilibrium physics within.

DC voltage switching-based octuple-electrode microdevice for cell rotation and area-specific membrane capacitance measurement

Single-cell electrorotation plays an important role in the field of single-cell imaging and electric parameter measurement. However, reported cell rotation technology often adopts a quadruple-electrode structure and is excited by an AC signal. The distribution of electric field strength in the enclosed area is not uniform, and the rotation speed of the cells is related to the location in the area, so it is difficult to achieve uniformity of electric field distribution and the stationarity of rotation. This work proposes a DC voltage switching-based octuple-electrode microdevice for cell rotation and area-specific membrane capacitance measurements. This design can switch the DC voltages on each electrode periodically to produce a uniformly distributed rotating electric field. The rotation direction of the electric field can be realized by simply controlling the switching order of the analog switches. According to the theoretical single-cell model, the area-specific membrane capacitance of cells are determined through rotation movements. Simultaneously, based on simulation results, the rotation area is normalized to enhance the accuracy of the measuring electrical parameters. This study demonstrates the potential application of the proposed octuple-electrode DC voltage-based electro-rotation device for rapid, convenient, and cost-effective manipulation and electrical parameter measurement of single cells.

Enhanced kinase translocation reporters for simultaneous real-time measurement of PKA, ERK, and Ca2.

Kinase translocation reporters (KTRs) are powerful tools for single-cell measurement of time-integrated kinase activity but suffer from restricted dynamic range and limited sensitivity, particularly in neurons. To address these limitations, we developed enhanced KTRs (eKTRs) for protein kinase A (PKA) and extracellular signal-regulated kinase (ERK) that display high sensitivity, rapid response kinetics, broad dynamic range, cell type-specific tuning, and an ability to detect PKA and ERK activity in primary sensory neurons. Moreover, co-expression of optically separable eKTRs for PKA and ERK revealed the kinetics of expected and unexpected crosstalk between PKA, ERK, protein kinase C, and calcium signaling pathways, demonstrating the utility of eKTRs for live cell monitoring of diverse and interacting signaling pathways. These results open the door to improved live-cell and in vivo measurements of key signaling pathways in neurons, while at the same time demonstrating the importance of KTR size and NLS strength to KTR dynamics.

Private information leakage from single-cell count matrices.

The increase in publicly available human single-cell datasets, encompassing millions of cells from many donors, has significantly enhanced our understanding of complex biological processes. However, the accessibility of these datasets raises significant privacy concerns. Due to the inherent noise in single-cell measurements and the scarcity of population-scale single-cell datasets, recent private information quantification studies have focused on bulk gene expression data sharing. To address this gap, we demonstrate that individuals in single-cell gene expression datasets are vulnerable to linking attacks, where attackers can infer their sensitive phenotypic information using publicly available tissue or cell-type-specific expression quantitative trait loci (eQTLs) information. We further develop a method for genotype prediction and genotype-phenotype linking that remains effective without relying on eQTL information. We show that variants from one study can be exploited to uncover private information about individuals in another study.

Entrainment and multi-stability of the p53 oscillator in human cells

The tumor suppressor p53 responds to cellular stress and activates transcription programs critical for regulating cell fate. DNA damage triggers oscillations in p53 levels with a robust period. Guided by the theory of synchronization and entrainment, we developed a mathematical model and experimental system to test the ability of the p53 oscillator to entrain to external drug pulses of various periods and strengths. We found that the p53 oscillator can be locked and entrained to a wide range of entrainment modes. External periods far from p53's natural oscillations increased the heterogeneity between individual cells whereas stronger inputs reduced it. Single-cell measurements allowed deriving the phase response curves (PRCs) and multiple Arnold tongues of p53. In addition, multi-stability and non-linear behaviors were mathematically predicted and experimentally detected, including mode hopping, period doubling, and chaos. Our work revealed critical dynamical properties of the p53 oscillator and provided insights into understanding and controlling it. Arecord of this paper's transparent peer review process is included in the supplemental information.

Automated High-Throughput Atomic Force Microscopy Single-Cell Nanomechanical Assay Enabled by Deep Learning-Based Optical Image Recognition.

Mechanical forces are essential for life activities, and the mechanical phenotypes of single cells are increasingly gaining attention. Atomic force microscopy (AFM) has been a standard method for single-cell nanomechanical assays, but its efficiency is limited due to its reliance on manual operation. Here, we present a study of deep learning image recognition-assisted AFM that enables automated high-throughput single-cell nanomechanical measurements. On the basis of the label-free identification of the cell structures and the AFM probe in optical bright-field images as well as the consequent automated movement of the sample stage and AFM probe, the AFM probe tip could be accurately and sequentially moved onto the specific parts of individual living cells to perform a single-cell indentation assay or single-cell force spectroscopy in a time-efficient manner. The study illustrates a promising method based on deep learning for achieving operator-independent high-throughput AFM single-cell nanomechanics, which will benefit the application of AFM in mechanobiology.

Lamellipodia-Mediated Osteoblast Haptotaxis Guided by Fibronectin Ligand Concentrations on a Multiplex Chip.

Skull morphogenesis is a complex, dynamic process involving two different germ layers and progressing to the coordinated, directional growth of individual bones. The mechanisms underlying directional growth toward the apex are not completely understood. Here, a microfluidic chip-based approach is utilized to test whether calvarial osteoblast progenitors undergo haptotaxis on a gradient of Fibronectin1 (FN1) via lamellipodia. Mimicking the embryonic cranial mesenchyme's FN1 pattern, FN1 gradients is established in the chip using computer modeling and fluorescent labeling. Primary mouse calvarial osteoblast progenitors are plated in the chip along an array of segmented gradients of adsorbed FN1. The study performs single-cell tracking and measures protrusive activity. Haptotaxis is observed at an intermediate FN1 concentration, with an average directional migration index (yFMI) of 0.07, showing a significant increase compared to the control average yFMI of -0.01. A significant increase in protrusive activity is observed during haptotaxis. Haptotaxis is an Arp2/3-dependent, lamellipodia-mediated process. Calvarial osteoblast progenitors treated with the Arp2/3 (Actin Related Protein 2/3 complex) inhibitor CK666 show significantly diminished haptotaxis, with an average yFMI of 0.01. Together, these results demonstrate haptotaxis on an FN1 gradient as a new mechanism in the apical expansion of calvarial osteoblast progenitors during development and shed light on the etiology of calvarial defects.

Multi-condition and multi-modal temporal profile inference during mouse embryonic development.

The emergence of single-cell time-series datasets enables modeling of changes in various types of cellular profiles over time. However, due to the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here we propose a joint modeling framework, Sunbear, for integrating multi-condition and multi-modal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-dataset and multi-modal profiles across time, and extrapolate single-cell profiles in a missing modality. We applied Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multi-modal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multi-modal and multi-condition views of cellular trajectories.

Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell.

With rapid development and mature of single-cell measurements, single-cell biology and pathology become an emerging discipline to understand the disease. However, it is important to address concerns raised by clinicians as to how to apply single-cell measurements for clinical practice, translate the signals of single-cell systems biology into determination of clinical phenotype, and predict patient response to therapies. The present Perspective proposes a new system coined as the clinical artificial intelligent single-cell (caiSC) with the dynamic generator of clinical single-cell informatics, artificial intelligent analyzers, molecular multimodal reference boxes, clinical inputs and outs, and AI-based computerization. This system provides reliable and rapid information for impacting clinical diagnoses, monitoring, and prediction of the disease at the single-cell level. The caiSC represents an important step and milestone to translate the single-cell measurement into clinical application, assist clinicians' decision-making, and improve the quality of medical services. There is increasing evidence to support the possibility of the caiSC proposal, since the corresponding biotechnologies associated with caiSCs are rapidly developed. Therefore, we call the special attention and efforts from various scientists and clinicians on the caiSCs and believe that the appearance of the caiSCs can shed light on the future of clinical molecular medicine.

Novel single-cell measurements suggest irreversibly sickled cells are neither dense nor dehydrated

In sickle cell anemia, deoxygenation causes erythrocytes to distort, while reoxygenation permits them to recover a normal biconcave disk shape. Irreversibly sickled cells (ISCs) remain distorted when reoxygenated and have been thought to have among the highest intracellular hemoglobin concentrations of the sickle red cell population and therefore the greatest vulnerability to vasoocclusion. Using a new optical method, which we describe, we have made precise measurements of the intracellular hemoglobin concentration, and intracellular O2 saturation, of ISCs, as well as oxygenated sickle cells with a normal biconcave disc shape, and cells with shapes distorted by the sickle fibers they contain. This method also provides good estimates of cell volumes, and hemoglobin per red cell. The concentration distribution of the ISCs is found to be similar to normal, discoid cells. Average ISC volumes exceed their discoid counterparts, with a much broader distribution, arguing against dehydration as their origin. The concentration distribution of the polymer-laden sickled cells is significantly higher in mean value, and their volume distributions indicate some dehydration. Previous assumptions about ISCs may have thus been colored by the presence of sickle cells that did contain polymer, and that true ISCs may be much more benign than once thought, which underscores the importance of accurate measurement on individual cells. This method could be used to follow changes in individual cell properties under various specific perturbations, and where characterization by flow cytometry is infeasible.

Cross-species imputation and comparison of single-cell transcriptomic profiles.

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

Single-cell measurement of microbial growth rate with Raman microspectroscopy.

Rates of microbial growth are fundamental to understanding environmental geochemistry and ecology. However, measuring the heterogeneity of microbial activity at the single-cell level, especially within complex populations and environmental matrices, remains a forefront challenge. Stable isotope probing (SIP) is a method for assessing microbial growth and involves measuring the incorporation of an isotopic label into microbial biomass. Here, we assess Raman microspectroscopy as a SIP technique, specifically focusing on the measurement of deuterium (2H), a tracer of microbial biomass production. We correlatively measured cells grown in varying concentrations of deuterated water with both Raman spectroscopy and nanoscale secondary ion mass spectrometry (nanoSIMS), generating isotopic calibrations of microbial 2H. Relative to Raman, we find that nanoSIMS measurements of 2H are subject to substantial dilution due to rapid exchange of H during sample washing. We apply our Raman-derived calibration to a numerical model of microbial growth, explicitly parameterizing the factors controlling growth rate quantification and demonstrating that Raman-SIP can sensitively measure the growth of microorganisms with doubling times ranging from hours to years. The measurement of single-cell growth with Raman spectroscopy, a rapid, nondestructive technique, represents an important step toward application of single-cell analysis into complex sample matrices or cellular assemblages.

Drop-by-Drop Addition of Reagents to a Double Emulsion.

Developments in droplet microfluidics have facilitated an era of high-throughput, sensitive single-cell, or single-molecule measurements capable of tackling the heterogeneity present in biological systems. Relying on single emulsion (SE) compartments, droplet assays achieve absolute quantification of nucleic acids, massively parallel single-cell profiling, and more. Double emulsions (DEs) have seen recent interest for their potential to build upon SE techniques. DEs are compatible with flow cytometry enabling high-throughput multi-parameter drop screening and eliminate content mixing due to coalescence during lengthy workflows. Despite these strengths, DEs lack important technical functions that exist in SEs such as methods for adding reagents to droplets on demand. Consequently, DEs cannot be used for multistep workflows which has limited their adoption in assay development. Here, strategies to enable reagent addition and other active manipulations on DEs are reported by converting DE inputs to SEs on chip. After conversion, drops are manipulated using existing SE techniques, including reagent addition, before reforming a DE at the outlet. Device designs and operation conditions achieving drop-by-drop reagent addition to DEs are identified and used as part of a multi-step aptamer screening assay performed entirely in DE drops. This work enables the further development of multistep DE droplet assays.

Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification

As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.

Data acquisition approaches for single cell proteomics.

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2.

Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

DMF-DM-seq: Digital-Microfluidics Enabled Dual-Modality Sequencing of Single-Cell mRNA and microRNA with High Integration, Sensitivity, and Automation.

Multimodal measurement of single cells provides deep insights into the intricate relationships between individual molecular layers and the regulatory mechanisms underlying intercellular variations. Here, we reported DMF-DM-seq, a highly integrated, sensitive, and automated platform for single-cell mRNA and microRNA (miRNA) co-sequencing based on digital microfluidics. This platform first integrates the processes of single-cell isolation, lysis, component separation, and simultaneous sequencing library preparation of mRNA and miRNA within a single DMF device. Compared with the current half-cell measuring strategy, DMF-DM-seq enables complete separation of single-cell mRNA and miRNA via a magnetic field application, resulting in a higher miRNA detection ability. DMF-DM-seq revealed differential expression patterns of single cells of noncancerous breast cells and noninvasive and aggressive breast cancer cells at both mRNA and miRNA levels. The results demonstrated the anticorrelated relationship between miRNA and their mRNA targets. Further, we unravel the tumor growth and metastasis-associated biological processes enriched by miRNA-targeted genes, along with important miRNA-interaction networks involved in significant signaling pathways. We also deconstruct the miRNA regulatory mechanisms underlying different signaling pathways across different breast cell types. In summary, DMF-DM-seq offers a powerful tool for a comprehensive study of the expression heterogeneity of single-cell mRNA and miRNA, which will be widely applied in basic and clinical research.

Constant surface area-to-volume ratio during cell growth as a design principle in mammalian cells.

All cells are subject to geometric constraints, such as surface area-to-volume (SA/V) ratio, that impact cell functions and force biological adaptations. Like the SA/V ratio of a sphere, it is generally assumed that the SA/V ratio of cells decreases as cell size increases. Here, we investigate this in near-spherical mammalian cells using single-cell measurements of cell mass and surface proteins, as well as imaging of plasma membrane morphology. We find that the SA/V ratio remains surprisingly constant as cells grow larger. This observation is largely independent of the cell cycle and the amount of cell growth. Consequently, cell growth results in increased plasma membrane folding, which simplifies cellular design by ensuring sufficient membrane area for cell division, nutrient uptake and deformation at all cell sizes.