Single-cell Precision Research Articles

AMAR-seq: Automated Multimodal Sequencing of DNA Methylation, Chromatin Accessibility, and RNA Expression with Single-Cell Resolution.

Parallel single-cell multimodal sequencing is the most intuitive and precise tool for cellular status research. In this study, we propose AMAR-seq to automate methylation, chromatin accessibility, and RNA expression coanalysis with single-cell precision. We validated the accuracy and robustness of AMAR-seq in comparison with standard single-omics methods. The high gene detection rate and genome coverage of AMAR-seq enabled us to establish a genome-wide gene expression regulatory atlas and triple-omics landscape with single base resolution and implement single-cell copy number variation analysis. Applying AMAR-seq to investigate the process of mouse embryonic stem cell differentiation, we revealed the dynamic coupling of the epigenome and transcriptome, which may contribute to unraveling the molecular mechanisms of early embryonic development. Collectively, we propose AMAR-seq for the in-depth and accurate establishment of single-cell multiomics regulatory patterns in a cost-effective, efficient, and automated manner, paving the way for insightful dissection of complex life processes.

Mid-Infrared Photoacoustic Stimulation of Neurons through Vibrational Excitation in Polydimethylsiloxane.

Photoacoustic (PA) emitters are emerging ultrasound sources offering high spatial resolution and ease of miniaturization. Thus far, PA emitters rely on electronic transitions of absorbers embedded in an expansion matrix such as polydimethylsiloxane (PDMS). Here, it is shown that mid-infrared vibrational excitation of C─H bonds in a transparent PDMS film can lead to efficient mid-infrared photoacoustic conversion (MIPA). MIPA shows 37.5 times more efficient than the commonly used PA emitters based on carbon nanotubes embedded in PDMS. Successful neural stimulation through MIPA both in a wide field with a size up to a 100µm radius and in single-cell precision is achieved. Owing to the low heat conductivity of PDMS, less than a 0.5°C temperature increase is found on the surface of a PDMS film during successful neural stimulation, suggesting a non-thermal mechanism. MIPA emitters allow repetitive wide-field neural stimulation, opening up opportunities for high-throughput screening of mechano-sensitive ion channels and regulators.

Photocatalytic manipulation of Ca2+ signaling for regulating cellular and animal behaviors via MOF-enabled H2O2 generation.

The in situ generation of H2O2 in cells in response to external stimulation has exceptional advantages in modulating intracellular Ca2+ dynamics, including high controllability and biological safety, but has been rarely explored. Here, we develop photocatalyst-based metal-organic frameworks (DCSA-MOFs) to modulate Ca2+ responses in cells, multicellular spheroids, and organs. By virtue of the efficient photocatalytic oxygen reduction to H2O2 without sacrificial agents, photoexcited DCSA-MOFs can rapidly trigger Ca2+ outflow from the endoplasmic reticulum with single-cell precision in a repeatable and controllable manner, enabling the propagation of intercellular Ca2+ waves (ICW) over long distances in two-dimensional and three-dimensional cell cultures. After photoexcitation, ICWs induced by DCSA-MOFs can activate neural activities in the optical tectum of tadpoles and thighs of spinal frogs, eliciting the corresponding motor behaviors. Our study offers a versatile optical nongenetic modulation technique that enables remote, repeatable, and controlled manipulation of cellular and animal behaviors.

High-dimensional deconstruction of ovarian cancer at single-cell precision reveals HEBP2 that reshape the TIME and drive carboplatin resistance

BackgroundSingle-cell sequencing was employed to analyze the tumor immune microenvironment in ovarian cancer (OC) patients, exploring the evolutionary roles of various macrophage subgroups in OC progression and their correlation with fatty acid metabolism-related genes in contributing to drug resistance. MethodsThis study aimed to decipher the mechanisms underlying OC chemoresistance (OC-CR) and carboplatin resistance by integrating and analyzing multiple single-cell RNA sequencing datasets from OC patients. The tumor immune microenvironment in OC-CR patients exhibited notable alterations in cellular interactions and the proportions of different immune cell populations, in contrast to the cohort sensitive to OC chemotherapy. ResultsThe study demonstrates that the fatty acid-associated gene HEBP2 not only accelerates OC progression but also modifies the immune landscape of OC, driving the polarization from M0_TAM to M2_TAM. This shift results in a diminished efficacy of chemotherapy in OC. Furthermore, both in vitro and in vivo experiments underscored HEBP2′s role in boosting the proliferation of OC-resistant cell lines and suppressing apoptosis, thereby facilitating carboplatin resistance. ConclusionIn conclusion, the immune microenvironments of OC-CR significantly differ from those sensitive to chemotherapy, underscoring HEBP2′s role in fostering OC resistance. This establishes HEBP2 as a promising prognostic marker and a novel target for therapeutic strategies against OC resistance.

Abstract 5527: Spatiotemporal analysis of growth factor regulation of osteosarcoma AKT and ERK signaling dynamics in an ex vivo lung metastasis model

Abstract In the present study, we investigated the relationships between spatiotemporal distribution of growth factors in the lung microenvironment, metastatic osteosarcoma cell signaling, and intratumoral gene expression heterogeneity. Osteosarcoma is an aggressive bone malignancy that commonly metastasizes to the lungs, resulting in significant patient mortality. During metastasis, disseminated cancer cells are exposed to microenvironment-derived growth factors that result in signaling pathway activation. Two such pathways are MAPK/ERK and PI3K/AKT, which regulate complex transcriptional programs underlying cell proliferation and survival decisions. In this study, we utilized precision cut lung slices (PCLS) obtained from mice with established osteosarcoma metastases to ex vivo model associations between the spatiotemporal distributions of growth factors in the lung, osteosarcoma signaling dynamics, and intratumoral gene expression heterogeneity, with single cell precision. To accomplish this, we engineered osteosarcoma cells that co-expressed genetically-encoded ERK and AKT sensors, engrafted them into PCLS tissues, then recorded sensor dynamics via live-cell microscopy. These tissues were subsequently fixed and immuno-stained for measurement of growth factor expression in resident lung cells and spatial correlation with observed tumor cell ERK-AKT signaling dynamics. Using this approach, we found that growth factor expression was diffusely increased within areas bordering osteosarcoma metastases. FGF2 and IGF1, which we have previously demonstrated to potently induce ERK signaling in osteosarcoma cells, were both significantly upregulated in these regions. However, despite relatively uniform overexpression of growth factors adjacent to tumors, individual osteosarcoma cells displayed stochastics patterns of ERK and AKT signaling, and downstream gene targets. These findings revealed that upregulation of lung growth factors, and the inherent systems-level properties of osteosarcoma signaling networks, can converge to promote intratumoral signaling and gene expression heterogeneity. Together, our results, using a novel PCLS and live-cell biosensor imaging approach, provide new insight into how spatial and temporal changes in microenvironmental signaling composition are integrated by metastatic osteosarcoma cells to promote heterogeneity. Citation Format: Rawan Makkawi, Ryan D. Roberts, Jeremy Copperman, Alexander E. Davies. Spatiotemporal analysis of growth factor regulation of osteosarcoma AKT and ERK signaling dynamics in an ex vivo lung metastasis model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5527.

Precise and scalable self-organization in mammalian pseudo-embryos.

Gene expression is inherently noisy, posing a challenge to understanding how precise and reproducible patterns of gene expression emerge in mammals. Here we investigate this phenomenon using gastruloids, a three-dimensional in vitro model for early mammalian development. Our study reveals intrinsic reproducibility in the self-organization of gastruloids, encompassing growth dynamics and gene expression patterns. We observe a remarkable degree of control over gene expression along the main body axis, with pattern boundaries positioned with single-cell precision. Furthermore, as gastruloids grow, both their physical proportions and gene expression patterns scale proportionally with system size. Notably, these properties emerge spontaneously in self-organizing cell aggregates, distinct from many in vivo systems constrained by fixed boundary conditions. Our findings shed light on the intricacies of developmental precision, reproducibility and size scaling within a mammalian system, suggesting that these phenomena might constitute fundamental features of multicellularity.

ScEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data.

The evolution in single-cell RNA sequencing (scRNA-seq) technology has opened a new avenue for researchers to inspect cellular heterogeneity with single-cell precision. One crucial aspect of this technology is cell-type annotation, which is fundamental for any subsequent analysis in single-cell data mining. Recently, the scientific community has seen a surge in the development of automatic annotation methods aimed at this task. However, these methods generally operate at a steady-state total cell-type capacity, significantly restricting the cell annotation systems'capacity for continuous knowledge acquisition. Furthermore, creating a unified scRNA-seq annotation system remains challenged by the need to progressively expand its understanding of ever-increasing cell-type concepts derived from a continuous data stream. In response to these challenges, this paper presents a novel and challenging setting for annotation, namely cell-type incremental annotation. This concept is designed to perpetually enhance cell-type knowledge, gleaned from continuously incoming data. This task encounters difficulty with data stream samples that can only be observed once, leading to catastrophic forgetting. To address this problem, we introduce our breakthrough methodology termed scEVOLVE, an incremental annotation method. This innovative approach is built upon the methodology of contrastive sample replay combined with the fundamental principle of partition confidence maximization. Specifically, we initially retain and replay sections of the old data in each subsequent training phase, then establish a unique prototypical learning objective to mitigate the cell-type imbalance problem, as an alternative to using cross-entropy. To effectively emulate a model that trains concurrently with complete data, we introduce a cell-type decorrelation strategy that efficiently scatters feature representations of each cell type uniformly. We constructed the scEVOLVE framework with simplicity and ease of integration into most deep softmax-based single-cell annotation methods. Thorough experiments conducted on a range of meticulously constructed benchmarks consistently prove that our methodology can incrementally learn numerous cell types over an extended period, outperforming other strategies that fail quickly. As far as our knowledge extends, this is the first attempt to propose and formulate an end-to-end algorithm framework to address this new, practical task. Additionally, scEVOLVE, coded in Python using the Pytorch machine-learning library, is freely accessible at https://github.com/aimeeyaoyao/scEVOLVE.

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.

Microbial consortium assembly and functional analysis via isotope labelling and single-cell manipulation of polycyclic aromatic hydrocarbon degraders.

Soil microbial flora constitutes a highly diverse and complex microbiome on Earth, often challenging to cultivation, with unclear metabolic mechanisms in situ. Here, we present a pioneering concept for the in situ construction of functional microbial consortia (FMCs) and introduce an innovative method for creating FMCs by utilizing phenanthrene as a model compound to elucidate their in situ biodegradation mechanisms. Our methodology involves single-cell identification, sorting, and culture of functional microorganisms, resulting in the formation of a precise in situ FMC. Through Raman-activated cell sorting-stable-isotope probing, we identified and isolated phenanthrene-degrading bacterial cells from Achromobacter sp. and Pseudomonas sp., achieving precise and controllable in situ consortia based on genome-guided cultivation. Our in situ FMC outperformed conventionally designed functional flora when tested in real soil, indicating its superior phenanthrene degradation capacity. We revealed that microorganisms with high degradation efficiency isolated through conventional methods may exhibit pollutant tolerance but lack actual degradation ability in natural environments. This finding highlights the potential to construct FMCs based on thorough elucidation of in situ functional degraders, thereby achieving sustained and efficient pollutant degradation. Single-cell sequencing linked degraders with their genes and metabolic pathways, providing insights regarding the construction of in situ FMCs. The consortium in situ comprising microorganisms with diverse phenanthrene metabolic pathways might offer distinct advantages for enhancing phenanthrene degradation efficiency, such as the division of labour and cooperation or communication among microbial species. Our approach underscores the importance of in situ, single-cell precision identification, isolation, and cultivation for comprehensive bacterial functional analysis and resource exploration, which can extend to investigate MFCs in archaea and fungi, clarifying FMC construction methods for element recycling and pollutant transformation in complex real-world ecosystems.

Tug-of-Peace: Visual Rivalry and Atypical Visual Motion Processing in MECP2 Duplication Syndrome of Autism.

Extracting common patterns of neural circuit computations in the autism spectrum and confirming them as a cause of specific core traits of autism is the first step toward identifying cell-level and circuit-level targets for effective clinical intervention. Studies in humans with autism have identified functional links and common anatomic substrates between core restricted behavioral repertoire, cognitive rigidity, and overstability of visual percepts during visual rivalry. To study these processes with single-cell precision and comprehensive neuronal population coverage, we developed the visual bistable perception paradigm for mice based on ambiguous moving plaid patterns consisting of two transparent gratings drifting at an angle of 120°. This results in spontaneous reversals of the perception between local component motion (plaid perceived as two separate moving grating components) and integrated global pattern motion (plaid perceived as a fused moving texture). This robust paradigm does not depend on the explicit report of the mouse, since the direction of the optokinetic nystagmus (OKN) is used to infer the dominant percept. Using this paradigm, we found that the rate of perceptual reversals between global and local motion interpretations is reduced in the methyl-CpG-binding protein 2 duplication syndrome (MECP2-ds) mouse model of autism. Moreover, the stability of local motion percepts is greatly increased in MECP2-ds mice at the expense of global motion percepts. Thus, our model reproduces a subclass of the core features in human autism (reduced rate of visual rivalry and atypical perception of visual motion). This further offers a well-controlled approach for dissecting neuronal circuits underlying these core features.

Imputation method for dropout in single-cell transcriptome data

Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.

Real-Time Micromotor Probe for Immune Neutrophil Activation State.

Neutrophil activation is a hallmark of the immune response. Approaches to identify neutrophil activation in real time are necessary but are still lacking. In this study, magnetic Spirulina micromotors are used as label-free probes that exhibit differences in motility under different neutrophil activation states. This is correlated with different secretions into the extracellular environment by activated/non-activated cells and local environmental viscoelasticity. The micromotor platform can bypass non-activated immune cells while being stopped by activated cells. Thus, the micromotors can serve as label-free biomechanical probes of the immune cell state. They can detect the activation state of target immune cells in real time and with single-cell precision, which provides new ideas for the diagnosis and treatment of diseases while deepening understanding of the biomechanics of activated immune cells.

Morphological profiling by high-throughput single-cell biophysical fractometry

Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles a smaller part of itself. Although fractal variations in cells are proven to be closely associated with the disease-related phenotypes that are otherwise obscured in the standard cell-based assays, fractal analysis with single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach that quantifies a multitude of single-cell biophysical fractal-related properties at subcellular resolution. Taking together with its high-throughput single-cell imaging performance (~10,000 cells/sec), this technique, termed single-cell biophysical fractometry, offers sufficient statistical power for delineating the cellular heterogeneity, in the context of lung-cancer cell subtype classification, drug response assays and cell-cycle progression tracking. Further correlative fractal analysis shows that single-cell biophysical fractometry can enrich the standard morphological profiling depth and spearhead systematic fractal analysis of how cell morphology encodes cellular health and pathological conditions.

Multiscale reconstruction of bronchus and cancer cells in human lung adenocarcinoma

BackgroundWhile previous studies primarily focused on the structure of the normal whole mouse lung, the whole bronchus and cytoarchitectural details of the mouse intact lung lobe have been discovered at single-cell resolution. Revealing the sophisticated lung adenocarcinoma structure at three-dimensional (3D) and single-cell level remains a fundamental and critical challenge for the pathological mechanism research of lung adenocarcinoma (LA).MethodsFluorescence micro-optical Sectioning Tomography (fMOST) combined with PI staining were used to obtain the 3D imaging of the human LA tissue at single-cell resolution.ResultsWith a spatial resolution of 0.32 × 0.32 × 1.0 μm3, the dataset of human LA with single-cell precision consists of two channels, each of which contains information about the bronchi and the cytoarchitecture. The bronchial wall is thicker and the lumen is smaller in the cancer tissue, in which its original normal structure is vanished. More solid components, more clustered cancer cells with larger nucleoli, and more significant atypia are found in cancer tissue. In paracancerous tissue, the bronchial wall cells have a monolayer or bilayer structure, cluster along the wall, and are relatively dispersed. Few fibrous structures and occasional dissemination of spread through air spaces (STAS) are observed.ConclusionsBased on the human LA tissue dataset obtained by fMOST and PI staining, the bronchi and cells were reconstructed and visualized. This work provides a technical roadmap for studying the bronchus and cytoarchitectural structure and their spatial relationship in LA tissue, which may help with the understanding of the main histological structure of LA among pathologists.

Upconversion nanoparticle-based optogenetic nanosystem for photodynamic therapy and cascade gene therapy.

Most photosensitizer molecules used for the photodynamic therapy (PDT) are chemically-synthesized organic photosensitizer dyes which show several limitations such as unsatisfactory cell uptake, weak selectivity and off-target phototoxicity. Recently, genetically-encoded photosensitizers have attracted increasing attentions which provide the targeted cell elimination with single-cell precision. However, their applications are mainly limited by the shallow tissue penetration depth of the excitation light and the low cell apoptosis ratio. Herein, we developed a feasible upconversion nanoparticle (UCNP)-based optogenetic nanosystem with three-in-one functional integration: bio-imaging, NIR-triggered PDT and cascade gene therapy. Firstly, the mitochondria-targeted genetically-encoded photosensitizer was constructed and transfected into cancer cells. Then, the functional upconversion nanoprobe was constructed with the mitochondria targetability and then the siRNA was loaded on the surface of UCNPs via the reactive oxygen species (ROSs) sensitive chemical bond. After the transfection and incubation, both of the upconversion nanoprobe and the genetically-encoded photosensitizer were accumulated in the mitochondria of cancer cells. Under the NIR irradiation, the emission of UCNPs could excite the expressed protein photosensitizer to generate ROSs which then stimulated the release of siRNAs in a controllable manner, achieving PDT and cascade gene therapy. Since the generation of ROSs and the release of siRNA occurred in the mitochondria in-situ, the mitochondria-mediated cell apoptosis signal pathway would be activated to induce cell apoptosis and subsequently inhibit tumor growth. To the best of our knowledge, this is the first report about NIR laser-activated, organelle-localized genetically-encoded photosensitizers developed for cascade therapy, which will widen the application of optogenetic tools in the tumor therapy. STATEMENT OF SIGNIFICANCE: The application of genetically-encoded photosensitizers in photodynamic therapy (PDT) is mainly limited by the shallow tissue penetration depth of the excitation light and unsatisfactory therapeutic performance. In this experiment, we developed an upconversion nanoparticles-based optogenetic nanosystem to enhance the PDT and cascade gene therapy for malignant tumors. The expressed genetically-encoded photosensitizers were accumulated in the mitochondria, which were activated in situ by the upconversion nanoprobe. Besides, the photogenerated reactive oxygen species (ROSs) stimulated the release of siRNAs in a controllable manner. To the best of our knowledge, this is the first report about NIR laser-activated, genetically-encoded photosensitizers developed for organelle-localized controllable cascade therapy. We hope this work can accelerate the application of genetically-encoded photosensitizers in the tumor therapy.

Abstract PR003: OXPHOS impairing mitochondrial DNA mutations suppress melanoma growth and metastatic progression

Abstract Cancers acquire somatic mitochondrial DNA (mtDNA) mutations undetected in surrounding tissues. Cancer-specific mtDNA mutations primarily constitute variants of unknown significance whose oncogenic consequences are poorly understood. Analysis of large-scale sequencing datasets suggests that human cancers generally select against severe mtDNA mutations; however, the presence of selective pressure on the mtDNA genome has yet to be experimentally demonstrated. Here we optimized a FACS-based protocol to reconstitute the highly metastatic human melanoma cell line A375 with exogenous mtDNA, thereby generating nuclear-isogenic cytoplasmic hybrid (cybrid) lines. Homoplasmic cybrids were generated exclusively harboring either exogenous wildtype mtDNA, mtDNA with partial loss of OXHPHOS function (SNPs in mt.ND1 (of Complex I) or mt.ATP6 (of Complex V)), or mtDNA with complete loss of OXPHOS function (derived from a human melanoma harboring a truncating frameshift mutation in mt.CO1 (of Complex IV)). Compared to wildtype cybrids, subcutaneous xenografts of ND1 and ATP6 cybrids into NOD–SCID Il2rg−/− (NSG) mice had elevated primary tumor ROS levels and similar primary tumor growth rates. Xenografts of our most dysfunctional OXPHOS cybrid, CO1, formed tumors albeit with lowered growth rates. Uniquely, the ND1 and CO1 xenografts respectively exhibited diminished metastatic burden and no metastatic burden. Our results suggest that OXPHOS impairing mtDNA mutations are tolerable during primary tumor growth but reduce the severity and incidence of metastasis. Lastly, we generated heteroplasmic A375 cybrids, harboring both wild-type and mutant alleles, and developed a ddPCR method to characterize allelic frequency with single-cell precision. Extended in vitro passage of the heteroplasmic cybrids failed to significantly shift the heteroplasmic mtDNA allelic frequency. However, following subcutaneous xenografting of the heteroplasmic cybrids, primary tumors significantly shifted their variant allele frequency to favor the wildtype allele. In summary, these results indicate that a functional mtDNA haplotype is not absolutely required for tumorigenesis but contributes to metastatic efficiency. In addition, we experimentally demonstrated the existence of selective pressure in favor of functional mtDNA during tumorigenesis. Citation Format: Spencer Shelton, Sara House, Vijayashree Ramesh, Zhengkang Chen, Claire Llamas, Alpaslan Tasdogan, Ralph DeBerardinis, Sean Morrison, Prashant Mishra. OXPHOS impairing mitochondrial DNA mutations suppress melanoma growth and metastatic progression [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr PR003.

Phenotypic heterogeneity in psoriatic arthritis: towards tissue pathology-based therapy.

Psoriatic arthritis (PsA) is a heterogeneous disease involving multiple potential tissue domains. Most outcome measures used so far in randomized clinical trials do not sufficiently reflect this domain heterogeneity. The concept that pathogenetic mechanisms might vary across tissues within a single disease, underpinning such phenotype diversity, could explain tissue-distinct levels of response to different therapies. In this Review, we discuss the tissue, cellular and molecular mechanisms that drive clinical heterogeneity in PsA phenotypes, and detail existing tissue-based research, including data generated using sophisticated interrogative technologies with single-cell precision. Finally, we discuss how these elements support the need for tissue-based therapy in PsA in the context of existing and new therapeutic modes of action, and the implications for future PsA trial outcomes and design.

Single-cell patterning and characterisation of antibiotic persistent bacteria using bio-sCAPA.

In microbiology, accessing single-cell information within large populations is pivotal. Here we introduce bio-sCAPA, a technique for patterning bacterial cells in defined geometric arrangements and monitoring their growth in various nutrient environments. We demonstrate bio-sCAPA with a study of subpopulations of antibiotic-tolerant bacteria, known as persister cells, which can survive exposure to high doses of antibiotics despite lacking any genetic resistance to the drug. Persister cells are associated with chronic and relapsing infections, yet are difficult to study due in part to a lack of scalable, single-cell characterisation methods. As >105 cells can be patterned on each template, and multiple templates can be patterned in parallel, bio-sCAPA allows for very rare population phenotypes to be monitored with single-cell precision across various environmental conditions. Using bio-sCAPA, we analysed the phenotypic characteristics of single Staphylococcus aureus cells tolerant to flucloxacillin and rifampicin killing. We find that antibiotic-tolerant S. aureus cells do not display significant heterogeneity in growth rate and are instead characterised by prolonged lag-time phenotypes alone.

Every gene everywhere all at once: High-precision measurement of 3D chromosome architecture with single-cell Hi-C.

The three-dimensional (3D) structure of chromosomes influences essential biological processes such as gene expression, genome replication, and DNA damage repair and has been implicated in many developmental and degenerative diseases. In the past two centuries, two complementary genres of technology—microscopy, such as fluorescence in situ hybridization (FISH), and biochemistry, such as chromosome conformation capture (3C or Hi-C)—have revealed general principles of chromosome folding in the cell nucleus. However, the extraordinary complexity and cell-to-cell variability of the chromosome structure necessitate new tools with genome-wide coverage and single-cell precision. In the past decade, single-cell Hi-C emerges as a new approach that builds upon yet conceptually differs from bulk Hi-C assays. Instead of measuring population-averaged statistical properties of chromosome folding, single-cell Hi-C works as a proximity-based “biochemical microscope” that measures actual 3D structures of individual genomes, revealing features hidden in bulk Hi-C such as radial organization, multi-way interactions, and chromosome intermingling. Single-cell Hi-C has been used to study highly dynamic processes such as the cell cycle, cell-type-specific chromosome architecture (“structure types”), and structure–expression interplay, deepening our understanding of DNA organization and function.

Controlled fabrication of functional liver spheroids with microfluidic flow cytometric printing

Multicellular liver spheroids are 3D culture models useful in the development of therapies for liver fibrosis. While these models can recapitulate fibrotic disease, current methods for generating them via random aggregation are uncontrolled, yielding spheroids of variable size, function, and utility. Here, we report fabrication of precision liver spheroids with microfluidic flow cytometric printing. Our approach fabricates spheroids cell-by-cell, yielding structures with exact numbers of different cell types. Because spheroid function depends on composition, our precision spheroids have superior functional uniformity, allowing more accurate and statistically significant screens compared to randomly generated spheroids. The approach produces thousands of spheroids per hour, and thus affords a scalable platform by which to manufacture single-cell precision spheroids for disease modeling and high throughput drug testing.