Single-cell Experiments Research Articles

A general framework for noise propagation in a cascade of metabolic transformations

This note investigates how noise propagates in cascades of metabolic transformations. Motivation stems from recent single cell experiments that have shown that noise generated in gene expression and enzymes fluctuations propagates to growth rate through metabolic fluxes. A stochastic approach based on Continuous-Time Markov Chains (CTMC) is exploited to model all reactions, with a special interest in the substrate production, assumed to happen in bursts. Different noise features are dealt with, including correlation of intermediate players, noise impact on the end-product and the role of a feedback from the end-product that may control the substrate production. Most results are given in terms of analytical solutions of the CTMC, in some cases exploiting linear approximations; in all these cases, the findings are validated via Monte Carlo stochastic simulations. The proposed results highlight how substrate production in bursts, cascade length and distance among species affect fluctuations and correlations, with the feedback possibly playing a crucial role in favor of noise propagation.

Quantitative Accuracy and Precision in Multiplexed Single-Cell Proteomics.

Single-cell proteomics workflows have considerably improved in sensitivity and reproducibility to characterize as-yet unknown biological phenomena. With the emergence of multiplexed single-cell proteomics, studies increasingly present single-cell measurements in conjunction with an abundant congruent carrier to improve the precursor selection and enhance identifications. While these extreme carrier spikes are often >100× more abundant than the investigated samples, the total ion current undoubtably increases but the quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e., <20×) and assess their elimination for a comparable sensitivity with superior accuracy. We find that subtle changes in the carrier ratio can severely impact the measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap while preserving acquisition throughput at an improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides the conceptualization of the optimal single-cell proteomics experiment.

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution, assess the efficacy of therapies and identify resistant subclones.We introduce LACE, the first algorithmic framework that processes single-cell mutational profiles from samples collected at different time points to reconstruct longitudinal models of cancer evolution. The approach maximizes a weighted likelihood function computed on longitudinal data points to solve a Boolean matrix factorization problem, via Markov chain Monte Carlo sampling.On simulations, LACE outperforms state-of-the-art methods for both bulk and single-cell sequencing data with respect to the reconstruction of the ground-truth clonal phylogeny and dynamics, also in conditions of unbalanced datasets, significant rates of sequencing errors and sampling limitations. As the results are robust with respect to data-specific errors, LACE is effective with mutational profiles generated by calling variants from (full-length) scRNA-seq data, and this allows one to investigate the relation between genomic and phenotypic evolution of tumors at the single-cell level.Here, we apply LACE to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, dissecting the impact of BRAF/MEK-inhibition on clonal evolution, also in terms of clone-specific gene expression dynamics. Furthermore, the analysis of breast cancer PDXs from longitudinal targeted scDNA-sequencing experiments delivers a high-resolution temporal characterization of intra-tumor heterogeneity.

Dynamics and Pathways of Chromosome Structural Organizations during Cell Transdifferentiation.

Direct conversion of one differentiated cell type into another is defined as cell transdifferentiation. In avoidance of forming pluripotency, cell transdifferentiation can reduce the potential risk of tumorigenicity, thus offering significant advantages over cell reprogramming in clinical applications. Until now, the mechanism of cell transdifferentiation is still largely unknown. It has been well recognized that cell transdifferentiation is determined by the underlying gene expression regulation, which relies on the accurate adaptation of the chromosome structure. To dissect the transdifferentiation at the molecular level, we develop a nonequilibrium landscape-switching model to investigate the chromosome structural dynamics during the state transitions between the human fibroblast and neuron cells. We uncover the high irreversibility of the transdifferentiation at the local chromosome structural ranges, where the topologically associating domains form. In contrast, the pathways in the two opposite directions of the transdifferentiation projected onto the chromosome compartment profiles are highly overlapped, indicating that the reversibility vanishes at the long-range chromosome structures. By calculating the contact strengths in the chromosome at the states along the paths, we observe strengthening contacts in compartment A concomitant with weakening contacts in compartment B at the early stages of the transdifferentiation. This further leads to adapting contacts toward the ones at the embryonic stem cell. In light of the intimate structure–function relationship at the chromosomal level, we suggest an increase of “stemness” during the transdifferentiation. In addition, we find that the neuron progenitor cell (NPC), a cell developmental state, is located on the transdifferentiation pathways projected onto the long-range chromosome contacts. The findings are consistent with the previous single-cell RNA sequencing experiment, where the NPC-like cell states were observed during the direct conversion of the fibroblast to neuron cells. Thus, we offer a promising microscopic and physical approach to study the cell transdifferentiation mechanism from the chromosome structural perspective.

Representation learning of RNA velocity reveals robust cell transitions

RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present Velocity Autoencoder (VeloAE), a tailored representation learning method, to learn a low-dimensional representation of RNA velocity on which cellular transitions can be robustly estimated. On various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture expected cellular differentiation in different biological systems. VeloAE, therefore, enhances the usefulness of RNA velocity for studying a wide range of biological processes.

Bioinformatics pipeline to advance the identification of transcription regulatory variants in LOAD noncoding regions

As new LOAD genetic risk loci are identified and more brain cell-type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single-cell molecular profiling experiments and validation using disease models and gene editing technologies. We developed a new bioinformatics pipeline to characterize and prioritize SNPs in enhancers located in LOAD-GWAS regions based on their predicted impact to alter transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD-GWAS regions to a filtered set of regulatory SNPs that have a predicted strong effect on TF binding. We utilized publicly available bioinformatics software and data sources. Software: motifbreakR, UCSC Table Browser, GTEx portal and JMP. Databases: dbSNP v150, chromatin state segmentation data from the Roadmap Consortium, expression data: brain tissue from GTEX v8, monocyte from Cardiogenics, TF ChIP-seq data from ENCODE and TF binding motifs from MotifDb. We catalogued 61 strong enhancers in LOAD-GWAS regions that encompass 326 SNPs and 104 TF binding sites. 77 and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF-binding sites showed ChIP-seq signals. Next, we evaluated the effect of SNPs mapped within this set of TF-binding sites and found that 11 SNPs interrupt with the TF binding. We then determined the LD relationships between the LOAD-risk SNP and the TF 'interrupter' SNP to interpret whether the LOAD association is driven by up- or down- regulation of transcription mediated by the corresponding TF. For example, a SNP within an active enhancer adjacent to PICALM disrupts the SPI1 TF binding site. The enhancer SNP and the LOAD-GWAS SNP are in high LD, and we found that the GWAS-risk allele links to the enhancer allele that causes loss of SPI1 binding. This study provides an analytical framework to catalogue noncoding variations in enhancers located in LOAD-GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for further exploration of their putative regulatory function using single-cell multi-omics assays and gene editing.

ScCODA is a Bayesian model for compositional single-cell data analysis

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA (https://github.com/theislab/scCODA), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses.

Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization.

Considerable effort has been devoted to refining experimental protocols to reduce levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17–31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.

Deciphering the Dynamic Molecular Program of Radiation-Induced Endothelial Senescence

Radiation-induced cellular senescence is a double-edged sword, acting as both a tumor suppression process limiting tumor proliferation, and a crucial process contributing to normal tissue injury. Endothelial cells play a role in normal tissue injury after radiation therapy. Recently, a study observed an accumulation of senescent endothelial cells (ECs) around radiation-induced lung focal lesions following stereotactic radiation injury in mice. However, the effect of radiation on EC senescence remains unclear because it depends on dose and fractionation, and because the senescent phenotype is heterogeneous and dynamic. Using a systems biology approach in vitro, we deciphered the dynamic senescence-associated transcriptional program induced by irradiation. Flow cytometry and single-cell RNA sequencing experiments revealed the heterogeneous senescent status of irradiated ECs and allowed to deciphered the molecular program involved in this status. We identified the Interleukin-1 signaling pathway as a key player in the radiation-induced premature senescence of ECs, as well as the endothelial-to-mesenchymal transition process, which shares strong hallmarks of senescence. Our work provides crucial information on the dynamics of the radiation-induced premature senescence process, the effect of the radiation dose, as well as the molecular program involved in the heterogeneous senescent status of ECs.

Molecular characterization of Barrett’s esophagus at single-cell resolution

Barrett's esophagus (BE) is categorized, based on morphological appearance, into different stages, which correlate with the risk of developing esophageal adenocarcinoma. More advanced stages are more likely to acquire chromosomal instabilities, but stage-specific markers remain elusive. Here, we performed single-cell DNA-sequencing experiments (scDNAseq) with fresh BE biopsies. Dysplastic BE cells frequently contained chromosomal instability (CIN) regions, and these CIN cells carried mutations corresponding to the COSMIC mutational signature SBS17, which were not present in biopsy-matched chromosomally stable (CS) cells or patient-matched nondiseased control cells. CS cells were predominantly found in nondysplastic BE biopsies. The single-base substitution (SBS) signatures of all CS BE cells analyzed were indistinguishable from those of nondiseased esophageal or gastric cells. Single-cell RNA-sequencing (scRNAseq) experiments with BE biopsies identified two sets of marker genes which facilitate the distinction between columnar BE epithelium and nondysplastic/dysplastic stages. Moreover, histological validation confirmed a correlation between increased CLDN2 expression and the presence of dysplastic BE stages. Our scDNAseq and scRNAseq datasets, which are a useful resource for the community, provide insight into the mutational landscape and gene expression pattern at different stages of BE development.

DDRE-36. INVESTIGATING MEDULLOBLASTOMA HETEROGENEITY AND PREDICTING COMPOUND RESPONSE

Abstract Medulloblastoma is the most common malignant brain tumor found in children. It is a cerebellar tumor that affects motor and cognitive processes such as coordination and movement. The standard of care is surgical removal, radiation, and chemotherapy. These treatments can be very damaging to the developing child, in that they can impair vision and walking, among other body functions. Due to this, new treatments are necessary. Treatment plans for children with medulloblastoma need to be tailored to the specific subtype that they have. Genetic studies have revealed that there are four subtypes of pediatric medulloblastoma: Group 3, Group 4, SHH, and WNT. Beyond these bulk-resolution subtypes, we hypothesize intratumor heterogeneity as a barrier to new effective treatments. I have mined single-cell RNA sequencing data to investigate cellular heterogeneity and predict compound response. I analyzed Medulloblastoma patient tumor data along with data obtained from a 10X Genomics Chromium single-cell RNA sequencing experiment performed in the laboratory from a Tg (Neurod-Smoothened*A1) mouse. We hypothesize that distinct cell populations within medulloblastoma should show different predicted compounds that would target them. We have ranked compound predictions to investigate whether compounds may selectively target any of these populations using transcriptional response signatures derived from the LINCS L1000 perturbagen-response dataset. We also hypothesize that Medulloblastoma tumors have distinct subtypes of cells that are preferentially sensitive to BET bromodomain, casein kinase, and ATM/ATR inhibitors. Our analysis identified ten transcriptionally distinct cell types across these medulloblastoma tumors as well as compounds predicted to target them in each transcriptional subtype. Furthermore, we identified bromodomain and casein kinase inhibitors as a potential combination therapy due to their predicted synergy at targeting all cell populations within medulloblastoma. Our studies show the importance of considering cellular heterogeneity when identifying new treatments for medulloblastoma and other brain cancers.

EPCO-18. A MESENCHYMAL CELL POPULATION MEDIATES RESISTANCE TO AURORA KINASE INHIBITORS IN GLIOBLASTOMA

Abstract Glioblastoma (GBM) remains the most common adult brain cancer, with a dismal average patient survival of less than two years. No new treatments have been approved for GBM since the introduction of the alkylating agent temozolomide in 2005. Even then, temozolomide treatment only increases the average survival of GBM patients by a few months. Thus, novel therapeutic options are direly needed. The aurora kinases A and B are targetable and overexpressed in GBM, and their expression is highly correlated with patient survival outcomes. Our lab has found that small molecule aurora kinase inhibition reduces GBM tumor growth in vitro and in vivo, however, eventually tumors still grow. Computational analysis integrating compound transcriptional response signatures from the LINCS L1000 dataset with the single-cell RNA-sequencing data of patient GBM tumors resected at the University of Miami predicts that aurora inhibition targets a subset of cells present within any GBM tumor. Results of in vivo single-cell perturbation experiments with the aurora kinase inhibitor alisertib coincide with our predictions and reveal a cellular transcriptional phenotype resistant to aurora kinase inhibition, characterized by a mesenchymal expression program. We find that small molecules that are predicted to target different cell populations from alisertib, including this resistant mesenchymal population, synergize with alisertib to kill GBM cells. As a whole, we have identified the cellular population resistant to aurora kinase inhibition and have developed an analytical framework that identifies synergistic small molecule combinations by identifying compounds that target transcriptionally distinct cellular populations within GBM tumors.

Modeling the Cellular Origin of EVI1 + MLL-AF9-Driven Acute Myeloid Leukemia (AML)

We previously reported that transplantation of hematopoietic stem cells (HSC) expressing a doxycycline (DOX)-regulated AML-associated MLL-AF9 fusion transgene can induce an invasive and chemoresistant disease in mice formed by tumour cells that express the transcription factor EVI1, a known marker of poor prognosis in AML and some solid cancers. To better understand the association of EVI1 and the cellular origin of the disease we analyzed Evi1-IRES-GFP reporter mice (female, n=8, 8-10 weeks old) and found that not only the, quiescent long-term hematopoietic stem cell (LT-HSC : lineage marker-negative (lin -) cKit +Sca1 +(= LSK), CD34 -CD135 -CD150 +CD48 -, 24±3.7%, n=8) compartment, but also more proliferating multipotent progenitors such as MPP1 (LSK, CD34 +CD135 -CD150 +CD48 -, 23±4.6%, n=8), MPP2 (LSK, CD34 +CD135 -CD150 +CD48 +, 6±2.3%, n=8) and MPP3 (LSK, CD34 +CD135 -CD150 -CD48 +, 2±1%, n=8) contain a significant number of cells that express abundant Evi1 (“Evi1 high”) at steady state. Notably, we did not observe any significant changes in numbers of Evi1 + cells nor levels of Evi1 mRNA expression in the LT-HSC and MPP1 compartments 5 days after DOX-mediated induction of the iMLL-AF9 fusion. To address the impact of Evi1 on clonogenic growth of iMLL-AF9-expressing LT-HSC, we plated Evi1 high and Evi1 low naïve cells in methylcellulose (MC) and found that upon addition of DOX, Evi1 high cells formed more colonies with an invasive morphology (“type IV”) compared to Evi1 low cells (n=11, p<0.05). Immunophenotypically, cells from Evi1 high cell-derived colonies retained a more immature phenotype, reflected by higher cKit +Sca1 + expression (n=11, p<0.05). Plated Evi1 high cells formed Evi1 + colonies whereas Evi1 low lost Evi1 expression (n=11, p<0.05). To address the differential transformation susceptibility in vivo, we transplanted identical numbers of naïve steady-state Evi1 + iMLL-AF9 LT-HSC and MPP1 into irradiated syngeneic recipients. While recipients of Evi1 + MPP1 cells developed an invasive AML earlier than Evi1 + LT-HSC-transplanted mice (n=27, median latency: 96.5 vs. 146.5d, n.s.), very similar disease phenotypes were observed. In contrast, transplants of Evi1 - MPP1 or LT-HSC resulted in a significantly delayed disease induction (n=31, median latency: >200d; LT-HSC: n.s.; MPP1: p<0.05). Although Evi1 + cell-induced disease did present with more extensive organ infiltration by leukemic blasts than Evi1 - AML the phenotypes were similar. We also wondered whether modulation of the HSC compartment by exogenous factors may change Evi1 expression and affect AML induction. We found that 2 days after a single injection (200mg/kg) of recombinant mouse thrombopoietin (mTPO) the number of LT-HSC (n=23; 647 vs. 1165/10 6 lin - cells, p<0.05), but not of MPP1, significantly increased. Similarly, a single application of the synthetic mTPO receptor agonist Romiplostim (RP, 200mg/kg) resulted after 48h in an increase of LT-HSC (n=14; 647 vs. 1459/10 6 lin - cells, p<0.0005). Likewise, a single dose (10mg/kg) of polyinosinic:polycytidylic acid (pI:pC) also significantly increased the number of LT-HSC (n=9; 460 vs. 2300/10 6 lin - cells, p<0.005) but not of MPP1 after 24h. In contrast, 5-Fluorouracil (5-FU; 150mg/kg) did not significantly change the number of LT-HSC and MPP1, 3- and 6-days post-injection. However, only mTPO and RP but not pI:pC or 5-FU significantly increased the fraction of Evi1 high expressing LT-HSC (23 vs. 50%, 23 vs. 49%; n=29, p<0.0001) and MPP1 (22 vs. 47%, 22 vs- 48%; n=29, p<0.0001). Transplantation of identical numbers of iMLL-AF9 LT-HSC and MPP1 isolated 2 days after mTPO application to the donors into irradiated syngeneic recipients resulted in a significantly faster induction of Evi1 + AML than controls (n=19, MPP1: 35 vs. 96.5d, p<0.0001; LT-HSC: 41 vs 146.5d, p<0.0001). Currently ongoing single-cell RNA sequencing experiments of LT-HSC and MPP1 with and without in vivo mTPO stimulation conditionally expressing the iMLL-AF9 fusion should provide some mechanistic insights into increased susceptibility for EVI1 + AML. Our results so far demonstrate that the dynamics of the HSC compartment critically affects the cellular origin and biology of MLL-AF9 driven AML. DisclosuresKurokawa: MSD K.K.: Research Funding, Speakers Bureau; Kyowa Hakko Kirin Co., Ltd.: Research Funding, Speakers Bureau; Eisai Co., Ltd.: Research Funding, Speakers Bureau; Otsuka Pharmaceutical Co., Ltd.: Research Funding, Speakers Bureau; ONO PHARMACEUTICAL CO., LTD.: Research Funding, Speakers Bureau; Takeda Pharmaceutical Company Limited.: Research Funding, Speakers Bureau; Chugai Pharmaceutical Company: Research Funding, Speakers Bureau; Astellas Pharma Inc.: Research Funding, Speakers Bureau; Pfizer Japan Inc.: Research Funding, Speakers Bureau; AbbVie GK: Research Funding, Speakers Bureau; Teijin Limited: Research Funding, Speakers Bureau; Daiichi Sankyo Company.: Research Funding, Speakers Bureau; Sumitomo Dainippon Pharma Co., Ltd.: Research Funding, Speakers Bureau; Nippon Shinyaku Co., Ltd.: Research Funding, Speakers Bureau.

Preservation of T-Cell Stemness with a Novel Expansionless CAR-T Manufacturing Process, Which Reduces Manufacturing Time to Less Than Two Days, Drives Enhanced CAR-T Cell Efficacy

Preservation of T-Cell Stemness with a Novel Expansionless CAR-T Manufacturing Process, Which Reduces Manufacturing Time to Less Than Two Days, Drives Enhanced CAR-T Cell Efficacy

Highly Multiplexed Cell Surface Immunophenotyping with Genotyping and Concurrent Transcriptomic Analysis of NPM1 mutated Acute Myeloid Leukemia

BACKGROUNDThe pathogenesis of acute myeloid leukemia (AML) is often attributed to the presence of somatic allelic variant(s) in hematopoietic stem/progenitor cells. However, malignant clones may have heterogenous cell-surface immunophenotypes including overlap with non-malignant cells. While leukemia-associated immunophenotypes and difference from normal approaches are used for flow cytometric assessment during and after treatment, such analysis may underrepresent true leukemia disease burden. Assessments of AML measurable residual disease (MRD) using flow cytometry and molecular methods have been reported as discrepant. Single-cell RNA sequencing experiments have recently attempted to distinguish malignant cells based on gene expression and/or immunophenotypic profiles alone. We hypothesized that single-cell genotyping of mutated transcript(s) coupled with broad surface proteome and transcriptome profiling could provide an integrated multimodal method for AML characterization. METHODSWe adapted the previously reported “genotyping of transcriptomes” (PMID: 31270458) to identify cells carrying the NPM1 type A mutation commonly seen, and typically stable throughout the disease course, in AML. Healthy human peripheral blood mononuclear cells (PBMC) were mixed with an AML cell line carrying NPM1 type A mutation (OCI-AML3) at 7:3 ratio and labelled with 163 oligo-tagged antibodies. Single cell 3'v3 gene expression- (GEX), antibody derived tag- (ADT) and genotyping of NPM1 (GNPM) -libraries (10X Genomics) were sequenced on the NovaSeq 6000 (Illumina). Results were processed using Seurat 4.0 toolkit. RESULTSIn total, 72% (n=1680) of barcoded cells could be genotyped for NPM1. Of the genotyped cells, 59% (n = 986) were not NPM1 mutated. Visualization using Uniform Manifold Approximation and Projection (UMAP) showed separation of healthy PBMCs and OCI-AML3 cells using protein data, confirmed by annotation using NPM1 genotyping (Figure 1). We found a significant positive correlation between mRNA and corresponding cell surface protein expressions in non-mutated (Pearson's coefficient, r = 0.502, p = 6.87e-11) and NPM1 mutated (r= 0.392, p= 7.5e-7) cells. Compared to non-mutated, NPM1 mutated cells showed nearly 14-fold higher NPM1 transcript levels. In addition, a total 63 proteins were highly expressed on the surface of NPM1 mutated cells (Figure 2). Among these, CD33 and CD36 showed maximum 8-fold increase in expression. Other highly expressed proteins with at least >2.5-fold change were cell adhesion molecules (including CD328, CD155, and CD56), extracellular matrix binding proteins (CD49a/b) and interleukin receptor (CD123). CONCLUSIONOverall, our results demonstrate proof of principle that high-throughput cell surface proteome, transcriptome and genotyping analysis can be simultaneously performed to comprehensively and confidently characterize individual AML cells. Patient-specific multiomics data with broad cell-surface proteomic screening may allow novel target identification for monitoring and/or therapeutic intervention. Ongoing work will now use this methodology to characterize a cohort of NPM1 mutated AML patient samples. [Display omitted] DisclosuresHourigan: Sellas: Research Funding.

CryoEM structure of the outer membrane secretin channel pIV from the f1 filamentous bacteriophage

The Ff family of filamentous bacteriophages infect gram-negative bacteria, but do not cause lysis of their host cell. Instead, new virions are extruded via the phage-encoded pIV protein, which has homology with bacterial secretins. Here, we determine the structure of pIV from the f1 filamentous bacteriophage at 2.7 Å resolution by cryo-electron microscopy, the first near-atomic structure of a phage secretin. Fifteen f1 pIV subunits assemble to form a gated channel in the bacterial outer membrane, with associated soluble domains projecting into the periplasm. We model channel opening and propose a mechanism for phage egress. By single-cell microfluidics experiments, we demonstrate the potential for secretins such as pIV to be used as adjuvants to increase the uptake and efficacy of antibiotics in bacteria. Finally, we compare the f1 pIV structure to its homologues to reveal similarities and differences between phage and bacterial secretins.

CellexalVR: A virtual reality platform to visualize and analyze single-cell omics data

SummarySingle-cell RNAseq is a routinely used method to explore heterogeneity within cell populations. Data from these experiments are often visualized using dimension reduction methods such as UMAP and tSNE, where each cell is projected in two or three dimensional space. Three-dimensional projections can be more informative for larger and complex datasets because they are less prone to merging and flattening similar cell-types/clusters together. However, visualizing and cross-comparing 3D projections using current software on conventional flat-screen displays is far from optimal as they are still essentially 2D, and lack meaningful interaction between the user and the data. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive virtual reality environment for the visualization and analysis of single-cell experiments that allows researchers to intuitively and collaboratively gain an understanding of their data.

Shape–velocity correlation defines polarization in migrating cell simulations

Cell migration plays essential roles in development, wound healing, diseases, and in the maintenance of a complex body. Experiments in collective cell migration generally measure quantities such as cell displacement and velocity. The observed short-time diffusion regime for mean square displacement in single-cell migration experiments on flat surfaces calls into question the definition of cell velocity and the measurement protocol. Theoretical results in stochastic modeling for single-cell migration have shown that this fast diffusive regime is explained by a white noise acting on displacement on the direction perpendicular to the migrating cell polarization axis (not on velocity). The prediction is that only the component of velocity parallel to the polarization axis is a well-defined quantity, with a robust measurement protocol. Here, we ask whether we can find a definition of a migrating-cell polarization that is able to predict the cell’s subsequent displacement, based on measurements of its shape. Supported by experimental evidence that cell nucleus lags behind the cell center of mass in a migrating cell, we propose a robust parametrization for cell migration where the distance between cell nucleus and the cell’s center of mass defines cell shape polarization. We tested the proposed methods by applying to a simulation model for three-dimensional cells performed in the CompuCell3D environment, previously shown to reproduce biological cells kinematics migrating on a flat surface.

Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures.

Simple SummaryPatients with Triple Negative Breast Cancer (TNBC) have a poor prognosis due to high inter-tumor heterogeneity and absence of effective targeted treatments. Through quantification of ongoing processes in each individual with TNBC, we propose an explanation on why certain previously suggested monotherapies, such as anti-EGFR, are not effective. We experimentally demonstrate that monotherapies or drug combinations that are not adjusted accurately to the patient-specific ongoing processes may create an evolutionary pressure on a tumor leading to the emergence of previously undetected or untargeted cellular subpopulations. We show for example that certain TNBC tumors may benefit from therapies targeting estrogen receptors (ER), similarly to ER positive cancers. When untargeted, those tumors may develop large ER positive subpopulations. We propose that anti-TNBC therapy should be accurately tailored to the personalized molecular processes and that incomplete or “wrong” treatments may generate diverse evolutionary routes of TNBC tumors leading to drug resistance.Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1–4 altered protein–protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development.

Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-cell Multi-omics Experiments.

Atherosclerosis is an inflammatory disease of the arterial regions exposed to disturbed blood flow (d-flow). D-flow regulates the expression of genes in the endothelium at the transcriptomic and epigenomic levels, resulting in proatherogenic responses. Recently, single-cell RNA sequencing (scRNAseq) and single-cell Assay for Transposase Accessible Chromatin sequencing (scATACseq) studies were performed to determine the transcriptomic and chromatin accessibility changes at a single-cell resolution using the mouse partial carotid ligation (PCL) model. As endothelial cells (ECs) represent a minor fraction of the total cell populations in the artery wall, a luminal digestion method was used to obtain EC-enriched single-cell preparations. For this study, mice were subjected to PCL surgery to induce d-flow in the left carotid artery (LCA) while using the right carotid artery (RCA) as a control. The carotid arteries were dissected out two days or two weeks post PCL surgery. The lumen of each carotid was subjected to collagenase digestion, and endothelial-enriched single cells or single nuclei were obtained. These single-cell and single-nuclei preparations were subsequently barcoded using a 10x Genomics microfluidic setup. The barcoded single-cells and single-nuclei were then utilized for RNA preparation, library generation, and sequencing on a high-throughput DNA sequencer. Post bioinformatics processing, the scRNAseq and scATACseq datasets identified various cell types from the luminal digestion, primarily consisting of ECs. Smooth muscle cells, fibroblasts, and immune cells were also present. This EC-enrichment method aided in understanding the effect of blood flow on the endothelium, which could have been difficult with the total artery digestion method. The EC-enriched single-cell preparation method can be used to perform single-cell omics studies in EC-knockouts and transgenic mice where the effect of blood flow on these genes has not been studied. Importantly, this technique can be adapted to isolate EC-enriched single cells from human artery explants to perform similar mechanistic studies.