Single-cell Parameters Research Articles

Illumina SBS Sequencing and DNBSEQ Perform Similarly for Single-Cell Transcriptomics

Background/Objectives: High-throughput single-cell RNA sequencing (scRNA-seq) workflows produce libraries that demand extensive sequencing. However, standard next-generation sequencing (NGS) methods remain expensive, contributing to the high running costs of single-cell experiments and often negatively affecting the sample numbers and statistical strength of such projects. In recent years, a plethora of new sequencing technologies have become available to researchers through several manufacturers, often providing lower-cost alternatives to standard NGS. Methods: In this study, we compared data generated from mouse scRNA-seq libraries sequenced with both standard Illumina sequencing by synthesis (Illumina SBS) and MGI’s DNA nanoball sequencing (DNBSEQ). Results: Our findings reveal similar overall performance using both technologies. DNBSEQ exhibited mildly superior sequence quality compared to Illumina SBS, as evidenced by higher Phred scores, lower read duplication rates and a greater number of genes mapping to the reference genome. Yet these improvements did not translate into meaningful differences in single-cell analysis parameters in our experiments, including detection of additional genes within cells, gene expression saturation levels and numbers of identified cells, with both technologies demonstrating equally robust performance in these aspects. The data produced by both sequencing platforms also produced comparable analytical outcomes for single-cell analysis. No significant difference in the annotation of cells into different cell types was observed and the same top genes were differentially expressed between populations and experimental conditions. Conclusions: Overall, our data demonstrate that alternative technologies can be applied to sequence scRNA-seq libraries, generating virtually indistinguishable results compared to standard methods, and providing cost-effective alternatives.

Biophysical phenotyping of single-cell based on impedance and application for individualized precision medicine

Single-cell biophysical characterization based on impedance measurement is an advantageous approach due to its label-free, high-efficiency, cost-effective and real-time capability. Biophysical phenotyping can yield timely and rich information on physiological and pathological state of cells for disease diagnosis, drug screening, precision medicine, etc. However, precise measurement on single-cell impedance is challenging, particularly hard to figure out the detailed biophysical parameters of single cell due to coupling and complexity of impedance model. Here, we propose an analytic determination method to decode single-cell electrophysiological parameters (including cell-substrate interface capacitance, cell membrane capacitance, cell membrane conductivity, and cytoplasm conductivity) from the impedances measured at optimized frequencies by using analytic solution rather than spectrum fitting. With this simple and fast analytic solution method, the physiological parameters of single cell in natural adhesion state can be accurately determined in real time. We validate this cell parameter determination method in monitoring the change of cell adhesion under hydraulic effects and exploring electrophysiological differences among MCF-7, HeLa, Huh7, and MDA-MB-231 cell lines. Particularly, we apply the approach to optimize tumor treating fields (TTFields) therapy, realizing individualized precision medicine. Our work provides an accurate and efficient approach for characterizing single-cell biophysical properties with real-time, in-situ, label-free, and less invasive advantages.

Electrical Characterization and Analysis of Single Cells and Related Applications.

Biological parameters extracted from electrical signals from various body parts have been used for many years to analyze the human body and its behavior. In addition, electrical signals from cancer cell lines, normal cells, and viruses, among others, have been widely used for the detection of various diseases. Single-cell parameters such as cell and cytoplasmic conductivity, relaxation frequency, and membrane capacitance are important. There are many techniques available to characterize biomaterials, such as nanotechnology, microstrip cavity resonance measurement, etc. This article reviews single-cell isolation and sorting techniques, such as the micropipette separation method, separation and sorting system (dual electrophoretic array system), DEPArray sorting system (dielectrophoretic array system), cell selector sorting system, and microfluidic and valve devices, and discusses their respective advantages and disadvantages. Furthermore, it summarizes common single-cell electrical manipulations, such as single-cell amperometry (SCA), electrical impedance sensing (EIS), impedance flow cytometry (IFC), cell-based electrical impedance (CEI), microelectromechanical systems (MEMS), and integrated microelectrode array (IMA). The article also enumerates the application and significance of single-cell electrochemical analysis from the perspectives of CTC liquid biopsy, recombinant adenovirus, tumor cells like lung cancer DTCs (LC-DTCs), and single-cell metabolomics analysis. The paper concludes with a discussion of the current limitations faced by single-cell analysis techniques along with future directions and potential application scenarios.

Floating-Electrode-Enabled Impedance Cytometry for Single-Cell 3D Localization

As a label-free, low-cost, and noninvasive tool, impedance measurement has been widely used in single-cell characterization analysis. However, due to the tiny volume of cells, the uncertainty of the spatial position in the microchannel will bring measurement errors in single-cell electrical parameters. To overcome the issue, we designed a novel microdevice configured with a coplanar differential electrode structure to accurately resolve the spatial position of single cells without constraining techniques such as additional sheath fluids or narrow microchannels. The device precisely localizes single cells by measuring the induced current generated by the combined action of the floating electrode and the differential electrodes when single cells flow through the electrode-sensing area. The device was experimentally validated by measuring 6 μm yeast cells and 10 μm particles, achieving spatial localization with a resolution down to 2.1 μm (about 5.3% of the channel width) in lateral direction and 1.2 μm (about 5.9% of the channel height) in the vertical direction at a flow rate of 1.2 μL/min. In addition, by comparing measurement of yeast cells and particles, it was demonstrated that the device not only localizes the single cells or particles but also simultaneously characterizes their status properties such as velocity and size. The device offers a competitive electrode configuration in impedance cytometry with the advantages of simple structure, low cost, and high throughput, promising cell localization and thus electrical characterization.

Development of Microfluidic Impedance Flow Cytometry Enabling the Quantification of Intrinsic Single-Cell Bio-Dielectric Parameters Based on Constrictional Microchannel, Numerical Simulation, and Neural Network

This study presents a microfluidic impedance flow cytometry enabling the quantification of intrinsic single-cell bio-dielectric parameters (e.g., cell radius of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {c}}$ </tex-math></inline-formula> , relative membrane permittivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\text {mem}}$ </tex-math></inline-formula> , and cytoplasmic conductivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {cyto}}$ </tex-math></inline-formula> ) based on constrictional microchannel, numerical simulation, and neural network. Numerical simulation of a traveling cell within constrictional microchannel was conducted to locate relationship between intrinsic bio-dielectric parameters of single cells with corresponding impedance profiles, functioning as “reference” cells for the feedforward neural network, producing fitting accuracies of 1.00 ± 0.00 of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {c}}$ </tex-math></inline-formula> , 1.00 ± 0.00 of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\text {mem}}$ </tex-math></inline-formula> , and 1.00 ± 0.01 of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {cyto}}$ </tex-math></inline-formula> . Then, the fitted analytical equation based on numerical simulation and neural network was further used to translate measured impedance profiles into intrinsic bio-dielectric parameters of single cells, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {c}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\text {mem}}$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {cyto}}$ </tex-math></inline-formula> were quantified as 7.6 ± <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.8 \mu \text{m}$ </tex-math></inline-formula> , 16.73 ± 2.97, and 1.38 ± 0.34 S/m of K562 cells ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{\text {cell}}=1284$ </tex-math></inline-formula> ), 6.4 ± <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.8 \mu \text{m}$ </tex-math></inline-formula> , 17.47 ± 5.09, and 1.46 ± 0.47 S/m of Jurkat cells ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{\text {cell}}=2116$ </tex-math></inline-formula> ), 5.9 ± <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7 \mu \text{m}$ </tex-math></inline-formula> , 17.40 ± 3.14, and 1.96 ± 0.81 S/m of HL-60 cells ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{\text {cell}}=3543$ </tex-math></inline-formula> ), respectively. Compared to phenomenal electrical signals (i.e., real and imaginary parts at specific frequency) detected by conventional electrical flow cytometry, which were prone to environmental variations, this microfluidic platform enabled the quantification of intrinsic bio-dielectric parameters of single cells and thus provides an effective tool in the field of flow cytometry.

Correction to: ‘TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data’ (2022) by Seidel &amp; Stadler

Correction to: ‘TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data’ (2022) by Seidel &amp; Stadler

CellVisioner: A Generalizable Cell Virtual Staining Toolbox based on Few-Shot Transfer Learning for Mechanobiological Analysis.

Visualizing cellular structures especially the cytoskeleton and the nucleus is crucial for understanding mechanobiology, but traditional fluorescence staining has inherent limitations such as phototoxicity and photobleaching. Virtual staining techniques provide an alternative approach to addressing these issues but often require substantial amount of user training data. In this study, we develop a generalizable cell virtual staining toolbox (termed CellVisioner) based on few-shot transfer learning that requires substantially reduced user training data. CellVisioner can virtually stain F-actin and nuclei for various types of cells and extract single-cell parameters relevant to mechanobiology research. Taking the label-free single-cell images as input, CellVisioner can predict cell mechanobiological status (e.g., Yes-associated protein nuclear/cytoplasmic ratio) and perform long-term monitoring for living cells. We envision that CellVisioner would be a powerful tool to facilitate on-site mechanobiological research.

Population distributions of single-cell adhesion parameters during the cell cycle from high-throughput robotic fluidic force microscopy

Single-cell adhesion plays an essential role in biological and biomedical sciences, but its precise measurement for a large number of cells is still a challenging task. At present, typical force measuring techniques usually offer low throughput, a few cells per day, and therefore are unable to uncover phenomena emerging at the population level. In this work, robotic fluidic force microscopy (FluidFM) was utilized to measure the adhesion parameters of cells in a high-throughput manner to study their population distributions in-depth. The investigated cell type was the genetically engineered HeLa Fucci construct with cell cycle-dependent expression of fluorescent proteins. This feature, combined with the high-throughput measurement made it possible for the first time to characterize the single-cell adhesion distributions at various stages of the cell cycle. It was found that parameters such as single-cell adhesion force and energy follow a lognormal population distribution. Therefore, conclusions based on adhesion data of a low number of cells or treating the population as normally distributed can be misleading. Moreover, we found that the cell area was significantly the smallest, and the area normalized maximal adhesion force was significantly the largest for the colorless cells (the mitotic (M) and early G1 phases). Notably, the parameter characterizing the elongation of the cells until the maximum level of force between the cell and its substratum was also dependent on the cell cycle, which quantity was the smallest for the colorless cells. A novel parameter, named the spring coefficient of the cell, was introduced as the fraction of maximal adhesion force and maximal cell elongation during the mechanical detachment, which was found to be significantly the largest for the colorless cells. Cells in the M phase adhere in atypical way, with so-called reticular adhesions, which are different from canonical focal adhesions. We first revealed that reticular adhesion can exert a higher force per unit area than canonical focal adhesions, and cells in this phase are significantly stiffer. The possible biological consequences of these findings were also discussed, together with the practical relevance of the observed population-level adhesion phenomena.

Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data

Background. Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data.Method. We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses.Result and Conclusion. We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.

Scalable inference of transcriptional kinetic parameters from MS2 time series data.

MotivationThe MS2-MCP (MS2 coat protein) live imaging system allows for visualization of transcription dynamics through the introduction of hairpin stem-loop sequences into a gene. A fluorescent signal at the site of nascent transcription in the nucleus quantifies mRNA production. Computational modelling can be used to infer the promoter states along with the kinetic parameters governing transcription, such as promoter switching frequency and polymerase loading rate. However, modelling of the fluorescent trace presents a challenge due its persistence; the observed fluorescence at a given time point depends on both current and previous promoter states. A compound state Hidden Markov Model (cpHMM) was recently introduced to allow inference of promoter activity from MS2-MCP data. However, the computational time for inference scales exponentially with gene length and the cpHMM is therefore not currently practical for application to many eukaryotic genes.ResultsWe present a scalable implementation of the cpHMM for fast inference of promoter activity and transcriptional kinetic parameters. This new method can model genes of arbitrary length through the use of a time-adaptive truncated compound state space. The truncated state space provides a good approximation to the full state space by retaining the most likely set of states at each time during the forward pass of the algorithm. Testing on MS2-MCP fluorescent data collected from early Drosophila melanogaster embryos indicates that the method provides accurate inference of kinetic parameters within a computationally feasible timeframe. The inferred promoter traces generated by the model can also be used to infer single-cell transcriptional parameters.Availability and implementationPython implementation is available at https://github.com/ManchesterBioinference/burstInfer, along with code to reproduce the examples presented here.Supplementary information Supplementary data are available at Bioinformatics online.

A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light

The circadian clock is a cellular mechanism that synchronizes various biological processes with respect to the time of the day. While much progress has been made characterizing the molecular mechanisms underlying this clock, it is less clear how external light cues influence the dynamics of the core clock mechanism and thereby entrain it with the light–dark cycle. Zebrafish-derived cell cultures possess clocks that are directly light-entrainable, thus providing an attractive laboratory model for circadian entrainment. Here, we have developed a stochastic oscillator model of the zebrafish circadian clock, which accounts for the core clock negative feedback loop, light input, and the proliferation of single-cell oscillator noise into population-level luminescence recordings. The model accurately predicts the entrainment dynamics observed in bioluminescent clock reporter assays upon exposure to a wide range of lighting conditions. Furthermore, we have applied the model to obtain refitted parameter sets for cell cultures exposed to a variety of pharmacological treatments and predict changes in single-cell oscillator parameters. Our work paves the way for model-based, large-scale screens for genetic or pharmacologically-induced modifications to the entrainment of circadian clock function.

Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images.

Multiplex imaging technologies are now routinely capable of measuring more than 40 antibody-labeled parameters in single cells. However, lateral spillage of signals in densely packed tissues presents an obstacle to the assignment of high-dimensional spatial features to individual cells for accurate cell-type annotation. We devised a method to correct for lateral spillage of cell surface markers between adjacent cells termed REinforcement Dynamic Spillover EliminAtion (REDSEA). The use of REDSEA decreased contaminating signals from neighboring cells. It improved the recovery of marker signals across both isotopic (i.e., Multiplexed Ion Beam Imaging) and immunofluorescent (i.e., Cyclic Immunofluorescence) multiplexed images resulting in a marked improvement in cell-type classification.

Calibration of single-cell model parameters based on membrane resistance improves the accuracy of cardiac tissue simulations

In the calibration process, replicating some cellular properties is the main focus, while the importance of membrane resistance (Rm) that is primal in the tissue-level modeling is often underestimated. Previously, we presented a framework in which Rm in addition to action potential (AP) waveform was considered in the cellular model fitting. In this paper, we test the hypothesis that this approach for tuning cellular model parameters improves the accuracy of simulations at the tissue level. In doing so, two different sets of single-cell models are generated via independent realizations of our multi-objective optimization approach. In the first set of calibration (Model I), root-mean-square error (RMSE) of AP, and absolute error (AE) of maximum upstroke velocity are included as optimization functions; however, in the second set of calibration (Model II), RMSE of Rm curve in the repolarization phase is also added to the optimization functions. The calibrated cell models are then used in several tissue configurations of physiological relevance. We adopt well-defined evaluation metrics to compare tissue models tuned using Models I and II. In the source-sink mismatch configuration, the average absolute relative error (ARE) of the critical transition border, defined as the smallest required window width between source and sink for AP propagation, is less than 4.7 % in Model II, while this error is increased to more than 8.9 % in Model I. In addition, in Model I, the average ARE of total time for activation of tissue is 3.3–6.3 %; however, in Model II, this error is reduced to 0.7–1.6 %. In the Purkinje-myocardium configuration, the average of RMSE of activation time map is reduced approximately 75 % in Model II. Finally, in the transmural APD heterogeneity configuration, the average AREs of AP duration (APD) and APD dispersion (i.e., the difference between maximum and minimum of APD) are about 13.2 % and 17.4 % in Model I and 5.8 % and 6.8 % in Model II, respectively. Overall, our results demonstrate that consideration of Rm in the single-cell optimization procedure yields a substantial improvement in the accuracy of tissue models.

Commentary on Some Recent Theses Relevant to Combating Aging: December 2020

Theses reviewed in this issue include "Advances on Chip-In-Cell Wireless Platform for Continuous Monitoring of Physiological Parameters in Single Cells," "Design and Delivery of Synthetic mRNA by a Peptide Nanoparticle," "Inflammation Alters Endothelial Progenitor Cell-derived Exosome Contents and Therapeutic Effect on Myocardial Repair," "Partial Reprogramming: A Shortcut to Rejuvenation," "Renal Angioplasty and Therapeutic Angiogenesis for Renovascular Disease: A Novel Treatment Strategy," and "Targeted EDTA Chelation Therapy with Albumin Nanoparticles to Reverse Arterial Calcification and Restore Vascular Health in Chronic Kidney Disease."

Carbohydrate-Based Micro/Nanocapsules With Controlled External Surface for Medical Applications.

Micro/nanocapsules would have many more applications if we were able to controllably populate their surface with various chemical moieties. The present review introduces a novel variant of interfacial polymerization (IP) as a very robust method of manufacturing reservoir micro/nanocapsules equipped with several different functionalities on the capsules' surface. We call the method—IPCESCO (Interfacial Polymerization for Capsules' External Surface Control). As always in IP, the capsules' forming reaction is between monomers dissolved in opposite phases (oil or water) and takes place at the interface. Each monomer carries two or more functionalities reacting with functional groups of the monomer dissolved in the other phase. IPCESCO requires that one or both monomers are additionally equipped with (protected) functional groups interfering neither with the payload nor with the polymer formation. These additional groups end up everywhere in the polymeric shell but most importantly they are present on the external surface of capsules. These “handles” allow for the introduction of various moieties onto the capsules' surface. Since carbohydrate chemists developed a plurality of protecting and deprotecting methods for various functional groups such as aldehyde and hydroxyl, modified mono, and oligosaccharides are particularly well-suited to act as monomers in IPCESCO. The article discusses possible monomers and their synthesis, the transformation of protected reactive groups on the external capsules' surface into the desired functionalities, the control of the number of moieties on the surface and the capsules surface's architecture. The most important application of the novel encapsulation technology is in drug delivery. Possible surface units facilitating capsules' transport in the body, delivery to specific locations and mechanisms of capsules rupture are also addressed. Other applications of novel capsules include an ultra-sensitive quantitation and removal of pathogens, transport of nutrients in plants, detection of various antigens and other parameters in single cells.

Methods and Sensors For Functional Genomic Studies of Cell‐Cycle Transitions in Single Cells

Our understanding of the genetic control mechanisms governing the cell cycle has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our fundamental understanding of much of what we know about how the cell cycle is regulated, these approaches also mask the signaling and genetic signatures of rare subpopulations. Quiescent (G0) or very slow dividing (SD) cells are two such related populations. Our data and that of others show that the majority of single clonal MCF7 breast cancer cells cultured at subconfluency can complete a full round of cell division in as little as 24h, however subpopulations within that culture (1–5%) can require over 100h to complete the cell cycle. Moreover, it is not clear how these phenotypes are transferred to daughter cells. Understanding the proteins and networks that control G0 or SD cells may lead to the identification of new drug targets for aging or tissue regeneration. This study establishes an integrated computational, imaging and molecular pipeline that enables the phenotyping of G0 or SD cultured cells within a population. We define imaging parameters and open source R scripts to generate and analyze time‐lapse images to track and quantify cell cycle transition in individual cells expressing a genetically encoded cell cycle reporter system (Ki67p‐FUCCI). We report how these cells can be used to identify transcriptomic signatures of G0, G1 and G2 phase cells within a population. Additionally, we present single‐cell microinjection parameters to optimize single cell transfection by direct injection of mRNA in MCF7 cells with detectable reporter gene expression (GFP) within 5h of microinjection. These findings coupled with the possibility of targeting several hundreds of single cells within a field of view while simultaneously conducting 4 channel time‐lapse microscopy dramatically increase throughput and sensitivity over conventional methods for functional genomics studies to identify new genes that control cell cycle transitions.

Multidimensional profiling of drug-treated cells by Imaging Mass Cytometry.

In pharmaceutical research, high‐content screening is an integral part of lead candidate development. Measuring drug response in vitro by examining over 40 parameters, including biomarkers, signaling molecules, cell morphological changes, proliferation indices, and toxicity in a single sample, could significantly enhance discovery of new therapeutics. As a proof of concept, we present here a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single‐cell quantitative information visualized as t‐SNE plots and heatmaps. Human breast cancer‐derived cell lines SKBR3, HCC1143, and MCF‐7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF‐7 cells dosed with three target‐specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole, and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3S28, Ki‐67, and p4E‐BP1T37/T46 in classified mitotic cells and anticorrelation with cell surface markers. Our study demonstrates that IMC data expand the number of measured parameters in single cells and brings higher‐dimension analysis to the field of cell‐based screening in early lead compound discovery.

MMHelper: An automated framework for the analysis of microscopy images acquired with the mother machine

Live-cell imaging in microfluidic devices now allows the investigation of cellular heterogeneity within microbial populations. In particular, the mother machine technology developed by Wang et al. has been widely employed to investigate single-cell physiological parameters including gene expression, growth rate, mutagenesis, and response to antibiotics. One of the advantages of the mother machine technology is the ability to generate vast amounts of images; however, the time consuming analysis of these images constitutes a severe bottleneck. Here we overcome this limitation by introducing MMHelper (https://doi.org/10.5281/zenodo.3254394), a publicly available custom software implemented in Python which allows the automated analysis of brightfield or phase contrast, and any associated fluorescence, images of bacteria confined in the mother machine. We show that cell data extracted via MMHelper from tens of thousands of individual cells imaged in brightfield are consistent with results obtained via semi-automated image analysis based on ImageJ. Furthermore, we benchmark our software capability in processing phase contrast images from other laboratories against other publicly available software. We demonstrate that MMHelper has over 90% detection efficiency for brightfield and phase contrast images and provides a new open-source platform for the extraction of single-bacterium data, including cell length, area, and fluorescence intensity.

Inferring neural circuit structure from datasets of heterogeneous tuning curves.

Tuning curves characterizing the response selectivities of biological neurons can exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or partially random connectivity also give rise to diverse tuning curves. Empirical tuning curve distributions can thus be utilized to make model-based inferences about the statistics of single-cell parameters and network connectivity. However, a general framework for such an inference or fitting procedure is lacking. We address this problem by proposing to view mechanistic network models as implicit generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable. Recent advances in machine learning provide ways for fitting implicit generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GANs) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic circuit models in theoretical neuroscience to datasets of tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, such as the biophysical parameters of, or synaptic connections between, particular recorded cells. Instead, it directly learns generalizable model parameters characterizing the network’s statistical structure such as the statistics of strength and spatial range of connections between different cell types. Another strength of this approach is that it fits the joint high-dimensional distribution of tuning curves, instead of matching a few summary statistics picked a priori by the user, resulting in a more accurate inference of circuit properties. More generally, this framework opens the door to direct model-based inference of circuit structure from data beyond single-cell tuning curves, such as simultaneous population recordings.

Flexible Trapping and Manipulation of Single Cells on a Chip by Modulating Phases and Amplitudes of Electrical Signals Applied onto Microelectrodes.

These days, multiplex assay with diverse functions on a single chip has become more and more imperative for biological cell research. Multipoint and multistep manipulation for single cells on a chip plays a significant role for cell characterization, immunoassays, and rare cell isolation, etc. In this article, a novel dielectrophoresis (DEP)-based manipulation method is proposed to flexibly move and position cells on a chip via applying various electrical signals onto microelectrodes. By modulating phases and amplitudes of alternating current (ac) signals applied onto the microelectrodes, single cells can be controllably moved from one position to another along diverse directions on a chip. Quantitative analysis is conducted for position and direction controls via simulation, which are validated through experiments. With this flexible manipulation method, single-cell biophysical parameters can be estimated in situ by moving the cell, and as an example, single HeLa and MCF-7 cells are measured. This method allows an efficient and flexible transportation of single cells in lab-on-a-chip systems and provides a fundamental platform for multioriented and multipoint manipulation. The chip is easy to scale up by means of array design for more multifunctional cell assays.