Biology subjects

Luo, X.

Publications and source records attributed to Luo, X..

At least 109 records · Page 6Linked to original sources

bioRxiv2024

Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank

BackgroundThe use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the ICD-10 from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies. ResultsWe investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain MRI feature sets) or socio-demographic features. Random forest classification models were adopted using rigorous shuffle splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest versus youngest) from the same feature sets and against additional classifier types (K-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features, and depression based on socio-demographic and functional neuroimaging features). ConclusionThese findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.

neuroscience↗

bioRxiv2024

Orbitofrontal cortex to dorsal striatum circuit is critical for incubation of oxycodone craving after forced abstinence

Relapse is a major challenge in treating opioid addiction, including oxycodone. During abstinence, oxycodone seeking progressively increases, and we previously demonstrated a causal role of the orbitofrontal cortex (OFC) in this incubation of oxycodone craving after forced abstinence. Here, we explored critical downstream targets of OFC by focusing on dorsal striatum (DS). We first examined dorsal striatal Fos (a neuronal activity marker) expression associated with oxycodone seeking after abstinence. Using a dopamine D1 receptor (D1R) antagonist, we also tested the causal role of DS in incubated oxycodone seeking. Next, we combined fluorescence-conjugated cholera toxin subunit B (CTb-555, a retrograde tracer) with Fos to assess whether the activation of OFC[->]DS projections was associated with incubated oxycodone seeking. We then used a series of pharmacological procedures to examine the causal role of the interaction between glutamatergic projections from OFC and D1R signaling in DS in incubation of oxycodone craving. We found that dorsal striatal Fos expression in DS exhibited a time-dependent increase in parallel with incubation of oxycodone craving, and DS inactivation decreased incubated oxycodone seeking. Moreover, OFC[->]DS projections were activated during incubated oxycodone seeking, and anatomical disconnection of OFC[->]DS projections, but not unilateral inactivation of OFC or DS, decreased incubated oxycodone seeking. Lastly, contralateral disconnection of OFC[->]DS projections had no effect on oxycodone seeking on abstinence day 1. Together, these results demonstrated a causal role of OFC[->]DS projections in incubation of oxycodone craving.

neuroscience↗

bioRxiv2024

BAITSAO: Building A Foundation Model for Drug Synergy Analysis Powered by Language Models

Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a novel unified Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models for the initial representation of drugs and cell lines. After demonstrating the relevance of these embeddings, we pre-train BAITSAO with a large-scale drug synergy database under a multi-task learning framework with rigorous selections of tasks. We demonstrate the superiority of the model architecture and the pre-trained strategies of BAITSAO over other methods through comprehensive benchmark analysis. Moreover, we investigate the sensitivity of BAITSAO and illustrate its unique functions including new drug discoveries, drug combinations-gene interaction, and multi-drug synergy predictions.

bioinformatics↗

bioRxiv2024

Biologically Interpretable VAE with Supervision for Transcriptomics Data Under Ordinal Perturbations

AO_SCPLOWBSTRACTC_SCPLOWLatent variable models such as the Variational Auto-Encoders (VAEs) have shown impressive performance for inferring expression patterns for cell subtyping and biomarker identification from transcriptomics data. However, the limited interpretability of their latent variables obscures deriving meaningful biological understanding of cellular responses to different external and internal perturbations. We here propose a novel deep learning framework, EXPORT (EXPlainable VAE for ORdinally perturbed Transcriptomics data), for analyzing ordinally perturbed transcriptomics data that can incorporate any biological pathway knowledge in the VAE latent space. With the corresponding pathway-informed decoder, the learned latent expression patterns can be explained as pathway-level responses to perturbations, offering direct interpretability with biological understanding. More importantly, we explicitly model the ordinal nature of many real-world perturbations into the EXPORT framework by training an auxiliary ordinal regressor neural network to capture corresponding expression changes in the VAE latent representations, for example under different dosage levels of radiation exposure. By incorporating ordinal constraints during the training of our proposed framework, we further enhance the model interpretability by guiding the VAE latent space to organize perturbation responses in a hierarchical manner. We demonstrate the utility of the inferred guided latent space for downstream tasks, such as identifying key regulatory pathways associated with specific perturbation changes by analyzing transcriptomics datasets on both bulk and single-cell data. Overall, we envision that our proposed approach can unravel unprecedented biological intricacies in cellular responses to various perturbations while bringing an additional layer of interpretability to biology-inspired deep learning models.

bioinformatics↗

bioRxiv2024

Development of an engineered extracellular vesicles-based vaccine platform for combined delivery of mRNA and protein to induce functional immunity

mRNA incorporated in lipid nanoparticles (LNPs) became a new class of vaccine modality for induction of immunity against COVID-19 and ushered in a new era in vaccine development. Here, we report a novel, easy-to-execute, and cost effective engineered extracellular vesicles (EVs)-based combined mRNA and protein vaccine platform (EVX-M+P vaccine) and explore its utility in proof-of-concept immunity studies in the settings of cancer and infectious disease. As a first example, we engineered EVs to contain ovalbumin mRNA and protein (EVOvaM+P) to serve as cancer vaccine against ovalbumin-expressing melanoma tumors. EVOvaM+P administration to mice with established melanoma tumors resulted in tumor regression associated with effective humoral and adaptive immune responses. As a second example, we generated engineered EVs, natural nanoparticle carriers shed by all cells, that contain mRNA and protein Spike (S) protein to serve as a combined mRNA and protein vaccine (EVSpikeM+P vaccine) against SARS-CoV-2 infection. EVSpikeM+P vaccine administration in mice and baboons elicited robust production of neutralizing IgG antibodies against RBD (receptor binding domain) of S protein and S protein specific T cell responses. Our proof-of-concept study describes a new platform with an ability for rapid development of combination mRNA and protein vaccines employing EVs for deployment against cancer and other diseases.

immunology↗

bioRxiv2024

Polygenic risk for depression and resting state functional connectivity of subgenual anterior cingulate cortex in young adults

Genetic variants may confer risks for depression by modulating brain structure and function. Prior evidence has underscored a key role of the subgenual anterior cingulate cortex (sgACC) in depression. Here, we built on the literature and examined how the resting state functional connectivity (rsFC) of the sgACC was associated with polygenic risks for depression. We followed published routines and computed seed-based whole-brain sgACC rsFC and polygenic risk scores (PRS) of 717 young adults curated from the Human Connectome Project. We performed whole-brain regression against PRS and severity of depression symptoms in a single model for all subjects and for men and women alone, controlling for age, sex (for all), race, severity of alcohol use, and household income, and evaluated the results at a corrected threshold. We found lower sgACC rsFC with the default mode network and frontal regions in association with PRS and lower sgACC-cerebellar rsFC in association with depression severity. We also noted sex differences in the connectivity correlates of PRS and depression severity. In an additional set of analyses, we observed a significant correlation between PRS and somatic complaints score and altered sgACC-somatosensory cortical connectivity in link with the severity of somatic complaints. Our findings collectively highlighted the pivotal role of distinct sgACC-based networks in the genetic predisposition to depression and the clinical manifestation of depression. Distinguishing the risk from severity markers of depression may have implications in developing early and effective treatments for individuals at risk for depression.

neuroscience↗

bioRxiv2024

Inflammatory pain resolution by mouse serum-derived small extracellular vesicles

Chronic pain is a significant public health issue. Current treatments have limited efficacy and significant side effects, warranting research on alternative strategies for pain management. One approach involves using small extracellular vesicles (sEVs) to transport beneficial biomolecular cargo to aid pain resolution. Exosomes are 30-150 nm sEVs that can carry RNAs, proteins, and lipid mediators to recipient cells via circulation. Exosomes can be beneficial or harmful depending on their source and contents. To investigate the short and long-term effects of mouse serum-derived sEVs in pain modulation, sEVs from naive control or spared nerve injury (SNI) model donor mice were injected intrathecally into naive recipient mice. Basal mechanical thresholds transiently increased in recipient mice. This effect was mediated by opioid signaling as this outcome was blocked by naltrexone. Mass Spectrometry of sEVs detected endogenous opioid peptide leu-enkephalin. A single prophylactic intrathecal injection of sEVs two weeks prior to induction of the pain model in recipient mice delayed mechanical allodynia in SNI model mice and accelerated recovery from inflammatory pain after complete Freunds adjuvant (CFA) injection. ChipCytometry of spinal cord and dorsal root ganglion (DRG) from sEV treated mice showed that prophylactic sEV treatment reduced the number of natural killer (NK) and NKT cells in spinal cord and increased CD206+ anti-inflammatory macrophages in (DRG) after CFA injection. Further characterization of sEVs showed the presence of immune markers suggesting that sEVs can exert immunomodulatory effects in recipient mice to promote the resolution of inflammatory pain. Collectively, these studies demonstrate multiple mechanisms by which sEVs can attenuate pain.

neuroscience↗

bioRxiv2024

Karyotype Description and Comparative Chromosomal Mapping of 5S rDNA in 21 species

5S rDNA is essential component of all cell types. A survey was conducted to study the 5S rDNA site number, position, and origin of signal pattern diversity in 64 plants using fluorescence in situ hybridization. The species used for this experiment were chosen due to the discovery of karyotype rearrangement, and for species checked in which 5S rDNA has not yet been explored. The chromosome number was 14-160, while the chromosome length was 0.63-6.88 m, with 37 plants (58%) had small chromosome (<3 m). The chromosome numbers of three species and 5S rDNA loci of 19 species have been reported for the first time. 5S rDNA was varied and abundant in signal site number (2-18), position (e.g., interstitial, distal, and proximal position, occasionally, outside chromosome), and even as intense (e.g., strong, weak, and slight). Our results exposed modifiability in the number and location of 5S rDNA in 33 plants and demonstrated an extensive stable number and location of 5S rDNA in 31 plants. The potential origin of signal pattern diversity was probably caused by chromosome rearrangement (e.g., deletion, duplication, inversion, translocation), polyploidization, self-incompatibility, and chromosome satellites. These data characterized the variability of 5S rDNA within the karyotypes of the 64 plants that exhibited massive chromosomal rearrangements and provided anchor points for genetic physical maps. These data prove the utility of the 5S rDNA oligonucleotide as a chromosome marker in identifying plant chromosomes. This method provides a basis for developing similar applications for cytogenetic analysis in other species. Article SummaryA survey was exploited to study 5S rDNA signal pattern diversity in 64 plants by FISH. The chromosome number of three species and 5S rDNA loci of 19 species have been reported by the first time. 5S rDNA was really rather various and abundant in signal site number (2-18), position (interstitial, distal, proximal position, occasionally, outside chromosome), even as the intense (strong, week, slight). These data prove the utility of 5S rDNA oligonucleotide as chromosome marker in identifying plant chromosomes.

molecular biology↗

bioRxiv2024

Sparse CBX2 nucleates many Polycomb proteins to promote facultative heterochromatinization of Polycomb target genes

Facultative heterochromatinization of genomic regulators by Polycomb repressive complex (PRC) 1 and 2 is essential in development and differentiation; however, the underlying molecular mechanisms remain obscure. Using genetic engineering, molecular approaches, and live-cell single-molecule imaging, we quantify the number of proteins within condensates formed through liquid-liquid phase separation (LLPS) and find that in mouse embryonic stem cells (mESCs), approximately 3 CBX2 proteins nucleate many PRC1 and PRC2 subunits to form one non-stoichiometric condensate. We demonstrate that sparse CBX2 prevents Polycomb proteins from migrating to constitutive heterochromatin, demarcates the spatial boundaries of facultative heterochromatin, controls the deposition of H3K27me3, regulates transcription, and impacts cellular differentiation. Furthermore, we show that LLPS of CBX2 is required for the demarcation and deposition of H3K27me3 and is essential for cellular differentiation. Our findings uncover new functional roles of LLPS in the formation of facultative heterochromatin and unravel a new mechanism by which low-abundant proteins nucleate many other proteins to form compartments that enable them to execute their functions.

molecular biology↗

bioRxiv2024

Comparative transcriptome analysis provides insights into the resistance regulation mechanism and inhibitory effect of fungicide phenamacril in Fusarium asiaticum

Fusarium asiaticum is a destructive phytopathogenic fungus that causes Fusarium head blight of wheat (FHB), leading to serious yield and economic losses to cereal crops worldwide. Our previous studies indicated that target-site mutations (K216R/E, S217P/L, or E420K/G/D) of Type I myosin FaMyo5 conferred high resistance to phenamacril. Here, we first constructed a sensitive strain H1S and point mutation resistant strains HA, HC and H1R. Then we conducted comparative transcriptome analysis of these strains in F. asiaticum after 1 g{middle dot}mL-1 and 10 g{middle dot}mL-1 phenamacril treatment. Results indicated that 2135 genes were differentially expressed (DEGs) among the sensitive and resistant strains. Among them, the DEGs encoding ammonium transporter MEP1/MEP2, nitrate reductase, copper amine oxidase 1, 4-aminobutyrate aminotransferase, amino-acid permease inda1, succinate-semialdehyde dehydrogenase, 2, 3-dihydroxybenzoic acid decarboxylase, etc., were significantly up-regulated in all the phenamacril-resistant strains. Compared to the control group, a total of 1778 and 2097 DEGs were identified in these strains after 1 g{middle dot}mL-1 and 10 g{middle dot}mL-1 phenamacril treatment, respectively. These DEGs involved in 4-aminobutyrate aminotransferase, chitin synthase 1, multiprotein-bridging factor 1, transcriptional regulatory protein pro-1, amino-acid permease inda1, ATP-dependent RNA helicase DED, acetyl-coenzyme A synthetase, sarcoplasmic/endoplasmic reticulum calcium ATPase 2, etc., showed significantly down-regulated expression in phenamacril-sensitive strain but not in resistant strains after phenamacril treatment. In addition, cyanide hydratase, mating-type protein MAT-1, putative purine nucleoside permease, plasma membrane protein yro2, etc., showed significantly co-down-regulated expression in all the strains after phenamacril treatment. Taken together, This study provide deep insights into the resistance regulation mechanism and inhibitory effect of fungicide phenamacril and these new annotated proteins or enzymes are worth for the discovery of new fungicide targets. Author summaryFungicide phenamacril resistance occur in F. asiaticum and the resistance regulation mechanis are systematic and complex. Here, we conducted comparative transcriptome analysis of a sensitive strain H1S and point mutation resistant strains HA, HC and H1R in F. asiaticum after 1 g{middle dot}mL-1 and 10 g{middle dot}mL-1 phenamacril treatment. Among these annotated proteins or enzymes, amino-acid permease inda1, 1, 4-aminobutyrate aminotransferase, chitin synthase 1, multiprotein-bridging factor 1, ATP-dependent RNA helicase DED, acetyl-coenzyme A synthetase, sarcoplasmic/endoplasmic reticulum calcium ATPase 2, cyanide hydratase, mating-type protein MAT-1, putative purine nucleoside permease, plasma membrane protein yro2, etc., were involved in the resistance regulation mechanism and inhibitory effect of fungicide phenamacril. Our paper provides a reference basis for the study of drug resistance in other microorganisms. In addition, the relevant proteins or enzymes annotated in our study also have reference value for the discovery of new fungicide targets.

pathology↗

bioRxiv2024

The inevitability and superfluousness of cell types in spatial cognition

Discoveries of functional cell types, exemplified by the cataloging of spatial cells in the hippocampal formation, are heralded as scientific breakthroughs. We question whether the identification of cell types based on human intuitions has scientific merit and suggest that "spatial cells" may arise in non-spatial computations of sufficient complexity. We show that deep neural networks (DNNs) for object recognition, which lack spatial grounding, contain numerous units resembling place, border, and head-direction cells. Strikingly, even untrained DNNs with randomized weights contained such units and support decoding of spatial information. Moreover, when these "spatial" units are excluded, spatial information can be decoded from the remaining DNN units, which highlights the superfluousness of cell types to spatial cognition. Now that large-scale simulations are feasible, the complexity of the brain should be respected and intuitive notions of cell type, which can be misleading and arise in any complex network, should be relegated to history.

neuroscience↗

bioRxiv2024

ThermoFinder: A sequence-based thermophilic proteins prediction framework

MotivationThermophilic proteins are important for academic research and industrial processes, and various computational methods have been developed to identify and screen them. However, their performance has been limited due to the lack of high-quality labeled data and efficient models for representing protein. Here, we proposed a novel sequence-based thermophilic proteins prediction framework, called ThermoFinder. ResultsIn this study, we demonstrated that ThermoFinder outperforms previous state-of-the-art tools on two benchmark datasets, and feature ablation experiments confirmed the effectiveness of our approach. Additionally, ThermoFinder exhibited exceptional performance and consistency across two newly constructed datasets, one of these was specifically constructed for the regression-based prediction of temperature optimum values directly derived from protein sequences. The feature importance analysis, using shapley additive explanations, further validated the advantages of ThermoFinder. We believe that ThermoFinder will be a valuable and comprehensive framework for predicting thermophilic proteins.

bioinformatics↗

bioRxiv2023

HyLight: Strain aware assembly of low coverage metagenomes

Different strains of identical species can vary substantially in terms of their spectrum of biomedically relevant phenotypes. Reconstructing the genomes of a microbial community at strain level poses major methodical challenges, because relative frequencies of individual strains match the rate of sequencing errors, which hampers the identification of their characteristic genetic variants. While next-generation sequencing (NGS) reads are too short to span complex repetitive regions, the considerably longer third-generation sequencing (TGS) reads are affected by larger sequencing error rates or are just significantly more expensive. Suppressing TGS coverage to limit costs implies losses in terms of the accuracy of the assemblies. Therefore, existing approaches have remained fragmentary: all approaches presented so far agree on losses in strain awareness, accuracy, possibly excessive costs of the assemblies, or combinations thereof. We present HyLight as, to the best of our knowledge, the first metagenome assembly approach that is not affected by any of the aforementioned drawbacks. In experiments, we demonstrate that HyLight assemblies are strain-aware, contiguous, contain little errors, and because operating on low coverage TGS data of the cheap kind, come at drastically reduced costs. HyLight implements hybrid assembly, which exploits the complementarity of TGS and NGS data. For unifying the two types of data, HyLight utilizes strain resolved overlap graphs (OG), which support the accurate reconstruction of the individual members of microbial communities at strain level: HyLight outperforms existing approaches in terms of strain identity preserving sequence by on average 25.53% (across all experiments / data sets: first quartile: 21.53%, median: 26.81%, third quartile: 31.98%), achieving near-complete strain awareness on many data sets. In summary, HyLight appears to implement the first protocol that delivers assemblies that are strain-aware, contiguous and accurate in combination.

bioinformatics↗

bioRxiv2023

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis

Various Foundation Models (FMs) have been built based on the pre-training and fine-tuning framework to analyze single-cell data with different degrees of success. In this manuscript, we propose a method named scELMo (Single-cell Embedding from Language Models), to analyze single-cell data that utilizes Large Language Models (LLMs) as a generator for both the description of metadata information and the embeddings for such descriptions. We combine the embeddings from LLMs with the raw data under the zero-shot learning framework to further extend its function by using the fine-tuning framework to handle different tasks. We demonstrate that scELMo is capable of cell clustering, batch effect correction, and cell-type annotation without training a new model. Moreover, the fine-tuning framework of scELMo can help with more challenging tasks including in-silico treatment analysis or modeling perturbation. scELMo has a lighter structure and lower requirements for resources. Our method also outperforms recent large-scale FMs (such as scGPT [1], Geneformer [2]) and other LLM-based single-cell data analysis pipelines (such as GenePT [3] and GPTCelltype [4]) based on our evaluations, suggesting a promising path for developing domain-specific FMs.

bioinformatics↗

bioRxiv2023

The Molecular Logic of Gtr1/2 and Pib2 Dependent TORC1 Regulation in Budding Yeast

The Target of Rapamycin kinase Complex I (TORC1) regulates cell growth and metabolism in eukaryotes. Previous studies have shown that, in Saccharomyces cerevisiae, nitrogen and amino acid signals activate TORC1 via the highly conserved small GTPases, Gtr1/2, and the phosphatidylinositol 3-phosphate binding protein, Pib2. However, it was unclear if/how Gtr1/2 and Pib2 cooperate to control TORC1. Here we report that this dual regulator system pushes TORC1 into at least three distinct signaling states: (i) a Gtr1/2 on, Pib2 on, rapid growth state in nutrient replete conditions; (ii) a Gtr1/2 inhibited, Pib2 on, adaptive/slow growth state in poor-quality growth medium; and (iii) a Gtr1/2 off, Pib2 off, quiescent state in starvation conditions. We suggest that other signaling pathways work in a similar way, to drive a multi-level response via a single kinase, but the behavior has been overlooked since most studies follow signaling to a single reporter protein.

molecular biology↗

bioRxiv2023

Satellite glial GPR37L1 regulates maresin and potassium channel signaling for pain control

G protein coupled receptor 37-like 1 (GPR37L1) is an orphan GPCR and its function remains largely unknown. Here we report that GPR37L1 transcript is highly expressed compared to all known GPCRs in mouse and human dorsal root ganglia (DRGs) and selectively expressed in satellite glial cells (SGCs). Peripheral neuropathy following diabetes and chemotherapy by streptozotocin and paclitaxel resulted in downregulations of surface GPR37L1 in mouse and human DRGs. Transgenic mice with Gpr37l1 deficiency exhibited impaired resolution of neuropathic pain symptom (mechanical allodynia), whereas overexpression of Gpr37l1 in mouse DRGs can reverse neuropathic pain. Notably, GPR37L1 is co-expressed and coupled with potassium channels in SGCs. We found striking species differences in potassium channel expression in SGCs, with predominant expression of KCNJ10 and KCNJ3 in mouse and human SGCs, respectively. GPR37L1 regulates the surface expression and function of KCNJ10 and KCNJ3. We identified the pro-resolving lipid mediator maresin 1 (MaR1) as a GPR37L1 ligand. MaR1 increases KCNJ10/KCNJ3-mediated potassium influx in SGCs via GPR37L1. MaR1 protected chemotherapy-induced suppression of KCNJ13/KCNJ10 expression and function in SGCs. Finally, genetic analysis revealed that the GPR37L1-E296K variant is associated with increased chronic pain risk by destabilizing the protein. Thus, GPR37L1 in SGCs offers a new target for neuropathy protection and pain control.

neuroscience↗

bioRxiv2023

Adaptive stretching of representations across brain regions and deep learning model layers

Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representations along task-relevant dimensions? We considered a task that required monkeys to selectively attend on a trial-by-trial basis to one of two dimensions (color or motion direction) to make a decision. Except for V4 (color bound) and MT (motion bound), the brain radically re-configured itself to stretch representations along task-relevant dimensions in lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, the model displayed task-relevant stretching as a consequence of error minimization, indicating that stretching is an adaptive strategy.

neuroscience↗

bioRxiv2023

Three-dimensional Isotropic Imaging of Live Suspension Cells Enabled by Droplet Microvortices

Three-dimensional (3D) imaging of non-adherent cells in suspension media is challenging due to their propensity to drift when not fixed to a substrate, as required by optical sectioning technologies. Resolution differences in the lateral versus depth directions typically present in those systems further complicates single-cell morphometry of cellular features indicative of effector functions, such as cytosol and organelle volumetric distribution, and cell membrane topography. Here, we present a method for 3D fluorescent isotropic imaging of live, non-adherent single cells encapsulated in picoliter droplets using Optical Projection Tomography (OPT) enabled by droplet microvortices. Our microfluidic platform features a droplet trap array that leverages flow-induced droplet interfacial shear to generate intra-droplet microvortices, which in turn are modulated to rotate single-cells on their axis to enable OPT-based imaging. This strategy allows observation of cells encapsulated inside non-toxic isotonic buffer droplets and facilitates scalable OPT acquisition by the simultaneous spinning of hundreds of cells. Specifically, we demonstrate 3D imaging of live myeloid and lymphoid cells in suspension, including K562 cells, as well as naive and activated T cells--small cells prone to movement in their suspended phenotype. In addition, morphometry of primary T cells under different immunological activation states allowed us to identify six distinct nuclear content distributions, which differ from the conventional 2D images depicting spheroid and bean-like nuclear shapes commonly associated with lymphocytes. This Arrayed-Droplet Optical Projection Tomography (ADOPT) technology is capable of isotropic, single live-cell 3D imaging and has the potential to perform large-scale morphometry of immune cell effector function states, while providing compatibility with microfluidic droplet operations.

biophysics↗