Biology subjects

Singh, R.

Publications and source records attributed to Singh, R..

At least 109 records · Page 6Linked to original sources

bioRxiv2024

Deep Multimodal Representations and Classification of First-Episode Psychosis via Live Face Processing

Schizophrenia is a severe psychiatric disorder associated with a wide range of cognitive and neurophysiological dysfunctions and long-term social difficulties. In this paper, we test the hypothesis that integration of multiple simultaneous acquisitions of neuroimaging, behavioral, and clinical information will be better for prediction of early psychosis than unimodal recordings. We propose a novel framework to investigate the neural underpinnings of the early psychosis symptoms (that can develop into Schizophrenia with age) using multimodal acquisitions of neural and behavioral recordings including functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and facial features. Our data acquisition paradigm is based on live face-to-face interaction in order to study the neural correlates of social cognition in first-episode psychosis (FEP). We propose a novel deep representation learning framework, Neural-PRISM, for learning joint multimodal compressed representations combining neural as well as behavioral recordings. These learned representations are subsequently used to describe, classify, and predict the severity of early psychosis in patients, as measured by the Positive and Negative Syndrome Scale (PANSS) and Global Assessment of Functioning (GAF) scores. We found that incorporating joint multimodal representations from fNIRS and EEG along with behavioral recordings enhances classification between typical controls and FEP individuals. Additionally, our results suggest that geometric and topological features such as curvatures and path signatures of the embedded trajectories of brain activity enable detection of discriminatory neural characteristics in early psychosis.

neuroscience↗

bioRxiv2024

Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC

Despite the widespread availability of genome sequencing pipelines, many genes remain part of the genome's "dark matter," where existing inference tools cannot even begin to guess the biological function of their proteins from sequence alone. This challenge is especially pronounced in organisms that are highly evolutionarily distant from well-studied models, where homology-based methods break down. Here, we describe PHILHARMONIC, a computational method that combines deep learning-based de novo protein interaction network inference with robust unsupervised spectral clustering and remote homology to illuminate functional organization in any non-model organism. From only a sequenced proteome, we show PHILHARMONIC generates meaningful functional hypothesis for protein functions, functional communities, and higher-order network structure. We validate its performance using experimental gene expression and pathway data in D. melanogaster, and we demonstrate its broad utility by analyzing temperature sensing and stress response pathways in the reef-building coral P. damicornis and its algal symbiont C. goreaui. PHILHARMONIC provides a general-purpose engine for functional discovery and biological hypothesis generation in non-model organisms, enabling systems-level insights across the full diversity of life.

bioinformatics↗

bioRxiv2024

scMultiNODE: Integrative Model for Multi-Modal Temporal Single-Cell Data

Measuring multiple genomic profiles (i.e., modalities) over time enhances our understanding of cell development. However, these modalities are not equally analyzable. Sparse and poorly separated measurements, such as chromatin accessibility, are difficult to interpret on their own, and the limited availability of temporal single-cell co-assay data leaves such modalities without the cross-modal cell correspondence needed to borrow structure from richer ones. As a result, critical developmental analyses and cross-modal comparisons remain underperformed for these weak modalities. Additionally, existing methods cannot integrate separately sequenced measurements while preserving cellular dynamics. We present scMultiNODE, an integration model that combines gene expression and chromatin accessibility measurements from multiple discrete timepoints for single cells, without relying on any prior cell-to-cell correspondence across modalities or time. scMultiNODE employs a scalable Quantized Gromov-Wasserstein optimal transport method to align cells across measurements, and neural ordinary differential equations with a dynamic regularization term to model cell development in a shared latent space, capturing cellular dynamics while preserving cell-type variations. Experiments across four developmental single-cell datasets demonstrate that scMultiNODE integrates separately sequenced measurements over time more effectively than existing methods that overlook cellular dynamics. Crucially, by anchoring chromatin accessibility to the shared dynamical latent space, scMultiNODE recovers developmental trajectories and cell annotations from scATAC-seq that this modality cannot support in isolation. The resulting joint latent space further supports important multi-modal downstream analyses, including complex trajectory investigation and cross-modal label transfer for cell annotation. The data, code, and supplementary notes are publicly available at https://github.com/rsinghlab/scMultiNODE.

bioinformatics↗

bioRxiv2024

LUNAR: A Deep Learning Model to Predict Glioma Recurrence Using Integrated Genomic and Clinical Data

Gliomas account for approximately 25.5% of all primary brain and central nervous system tumors, with a striking 80.8% of these being malignant. The prognosis varies significantly; low-grade gliomas (LGGs) can exhibit 5-year survival rates of up to 80%, while higher-grade gliomas (HGGs) often see rates below 5%. Recurrence is a common challenge, occurring in 52%-62% of LGGs and 90% of HGGs, complicating clinical management and treatment planning. Currently, no widely available models exist for predicting glioma recurrence, which is critical for optimizing patient outcomes. Machine learning (ML) and deep learning (DL) techniques have shown promise in predicting recurrence for various cancers, most using Electronic Health Records (EHR). This study introduces gLioma recUrreNce Attention-based classifieR (LUNAR), a DL-based model to predict early versus late glioma recurrence by integrating clinical, genomic, and mRNA expression data from patients with primary grade II-IV gliomas. The data was obtained from The Cancer Genome Atlas and the Glioma Longitudinal Analysis Consortium (GLASS).

bioinformatics↗

bioRxiv2024

Novel Mechanism of Inflammatory Activation by Ebola Virus Matrix Protein Linked to the Ebolavirus Virulence

Uncontrolled systemic inflammatory responses are a critical pathological feature of fatal Ebola virus (EBOV) infection. While some inflammatory responses may originate from mononuclear phagocytes (MNPs), non-immune cells vastly outnumber MNPs and may be an important source of inflammation. Here, we demonstrated that highly virulent EBOV induced a high and sustained pro-inflammatory response compared to less virulent ebolaviruses in non-MNPs through TLR4-independent NF-{kappa}B activation. We identified the EBOV matrix protein VP40 as a potent activator of NF-{kappa}B in non-MNPs, whose intrinsic inflammatory activation ability is higher than VP40 proteins from less virulent ebolaviruses. This suggests that VP40 is a novel virulence determinant inducing distinct degrees of pro-inflammatory responses among ebolaviruses. Mechanistically, VP40 activated the NF-{kappa}B signaling pathway, primarily via TNFR1 using a ligand-independent mechanism. These findings reveal mechanisms that may drive systemic inflammation and promote EBOV pathogenesis, suggesting potential therapeutic strategies to mitigate immune dysregulation in severe EBOV infections.

microbiology↗

bioRxiv2024

Molecular basis of autoimmune disease protection by MDA5 variants

MDA5 recognizes dsRNA from viruses and retroelements. Cooperative filament formation and ATP-dependent proofreading confer MDA5 with the necessary sensitivity and specificity for dsRNA. The gene encoding MDA5 is a hotspot for disease-associated variants. Many MDA5 variants are associated with protection from autoimmune disease, while increasing the risk of infection and chronic inflammation, but how these variants affect MDA5-dependent RNA sensing remains unclear. Here, we determine the consequences of autoimmune-protective MDA5 variants on the molecular structure and activities of MDA5. The rare variants E627* and I923V reduce the cellular interferon response to picornavirus infection and are deficient in filament formation. The I923V variant is ATPase hyperactive, causing premature dissociation from dsRNA. Cryo-EM structures of MDA5 I923V bound to dsRNA at different stages of ATP hydrolysis reveal smaller RNA binding interfaces, leading to excessive proofreading activity. Variants R843H and T946A, which are genetically linked and cause mild phenotypes, have no effect on dsRNA recognition, suggesting an indirect disease mechanism. We conclude that the autoimmune-protective MDA5 variants lead to a loss of MDA5-dependent signaling via multiple distinct mechanisms.

biochemistry↗

bioRxiv2024

Allo-Allo: Data-efficient prediction of allosteric sites

Allostery, a fundamental structural mechanism where ligand binding at a protein site affects protein function at another site, plays a crucial role in key drug-target proteins like GPCRs. Unfortunately, existing methods for predicting allosteric sites have limited performance- they are particularly constrained by scarce ground-truth experimental data. We introduce Allo-Allo, a data-efficient, sequence-based method that predicts allosteric sites by leveraging protein language models (PLMs). Honing in on ESM-2 attention heads that capture allosteric residue associations, Allo-Allo achieves a 67% higher AUPRC than state-of-the-art methods. Our innovative, data-efficient pipeline not only outperforms alternate, commonly-used PLM-based prediction architectures but also generalizes well. Notably, mutations in Allo-Allo-predicted sites show significant association with elevated disease risk scores from AlphaMissense, highlighting its translational potential. Beyond Allo-Allos biological and translational applicability, its architecture presents a powerful framework for other data-scarce problems in protein analysis.

bioinformatics↗

bioRxiv2024

Spatial programming of fibroblasts promotes resolution of tissue inflammation through immune cell exclusion

The role of fibroblasts in determining tissue topography and immune cell organisation within chronically inflamed tissues is poorly understood. Herein, we use multi-omic spatial analysis to define the cellular zonation pattern of the synovium in patients with inflammatory arthritis, identifying discrete tissue niches underpinned by spatially programmed subsets of synovial fibroblasts. We observe that perivascular fibroblasts switch on distinct matrix programs in response to cytokine signalling from neighbouring cells, forming adapted tissue niches that either permit or restrict immune cell trafficking. Specifically, IFN-{gamma}-responsive fibroblasts form a pathogenic lymphocyte-permissive niche that supports the persistence of leukocytes in the tissue, whilst TGF-{beta}-responsive, matrix-synthesising fibroblasts comprise a reparative niche, composed of a collagen-rich barrier around blood vessels that restricts leukocyte migration and promotes resolution of tissue inflammation. Augmentation of such endogenous pathways to promote resolution of inflammation may offer therapeutically tractable approaches for restoration of tissue homeostasis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/614064v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1e6e4dborg.highwire.dtl.DTLVardef@1baee04org.highwire.dtl.DTLVardef@1608a1forg.highwire.dtl.DTLVardef@10c1edb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

immunology↗

bioRxiv2024

One Health assessment of poultry and cattle farms as reservoirs for ESBL-producing Escherichia coli

BACKGROUNDUnregulated use of antibiotics in animal food production leads to the emergence of multidrug resistant bacteria, notably extended-spectrum beta-lactamases producing Escherichia coli (ESBL E. coli). ESBLs severely limit treatment options and pose a significant threat to public health. Investigating the potential role of livestock and poultry farms in harboring ESBL E. coli is crucial, given the prevalence of ESBL E. coli in humans, animals, and the environment, following the One Health framework. OBJECTIVESThe aim of this study was to assess and compare the potential contribution of cattle and poultry farms along with their respective human handlers to the emergence and spread of ESBL E. coli following One Health approach. METHODSTwenty cattle and twenty poultry farms were surveyed in Ludhiana, India to determine antibiotic usage, prevalent diseases in the farms. Animal faeces/poultry droppings, handler hand swabs, and stool samples were examined for the presence of ESBL E. coli. The isolates were examined for their susceptibility to 16 antibiotics and for the presence of selected antimicrobial resistance genes, including ESBL genes. Generalized linear mixed models were applied to investigate variations in the prevalence of ESBL E. coli associated with farm animal species, farm practices, and farm biosecurity measures. RESULTSOur findings revealed a higher prevalence of ESBL E. coli in poultry farms (53.75%) than in cattle farms (40.72%) and overall lower prevalence (12%) in human handlers. Of the total isolates, 86.52% were multidrug resistant, and 93.91% showed a high multiple antibiotic resistance (MAR) index (>0.2). Isolates from poultry farms exhibited a higher level of resistance with 97% of isolates exhibiting multidrug resistance, 100% isolates with MAR index of more than 0.2, and 90.70% carrying the blaCTXM gene. CONCLUSIONOur findings highlight cattle and poultry farms as potential reservoirs for multidrug-resistant and ESBL E. coli, contributing to the rising environmental burden of antibiotic resistance genes. This emphasizes the urgency of limiting antibiotic use in animal farming, especially in poultry production, to address the escalating problem of antibiotic resistance and protect both animal and human health.

microbiology↗

bioRxiv2024

Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation

Inferring gene regulatory networks from gene expression data is an important and challenging problem in the biology community. We propose OTVelo, a methodology that takes time-stamped single-cell gene expression data as input and predicts gene regulation across two time points. It is known that the rate of change of gene expression, which we will refer to as gene velocity, provides crucial information that enhances such inference; however, this information is not always available due to the limitations in sequencing depth. Our algorithm overcomes this limitation by estimating gene velocities using optimal transport. We then infer gene regulation using time-lagged correlation and Granger causality via regularized linear regression. Instead of providing an aggregated network across all time points, our method uncovers the underlying dynamical mechanism across time points. We validate our algorithm on 13 simulated datasets with both synthetic and curated networks and demonstrate its efficacy on 4 experimental data sets. Author summaryUnderstanding how genes interact to regulate cellular functions is crucial for advancing our knowledge of biology and disease. We present OTVelo, a method that uses single-cell gene expression data collected at different time points to infer gene regulatory networks. OTVelo offers a dynamic view of how gene interactions change over time, providing deeper insights into cellular processes. Unlike traditional methods, OTVelo captures temporal information through ancestor-descendant transitions without assuming a specific underlying regulatory model. We validate our approach using both simulated and real-world data, demonstrating its effectiveness in revealing complex gene regulation patterns. This method could lead to new discoveries in understanding biological systems and developing disease treatments.

bioinformatics↗

bioRxiv2024

Screening of histone mutants reveals a domain within the N-terminal tail of histone H3 that regulates the Tup1-independent repressive role of Cyc8 at the active FLO1

Yeast flocculation relies on cell surface flocculin proteins encoded by the FLO1 gene. The expression of FLO1 is antagonistically regulated by the Tup1-Cyc8 and the Swi-Snf complexes. The Post translational modifications of core histones regulate the transcription of Tup1-Cyc8-regulated genes. However, the mechanisms by which the physical presence of tail residues regulate FLO1 transcription process and flocculation is yet to be completely understood. Through screening we have identified a new region within the N-terminal tail of histone H3 regulating the transcription of FLO1 and FLO5. One of the histone H3 N-terminal truncation mutants H3{Delta}(17-24) showed higher FLO1 expression compared to wild-type H3. Results revealed that in absence of 17-24 stretch the occupancy of Cyc8 decreases from the upstream regions of FLO1. Additionally, analysis suggests that Hda1 is required for the Cyc8-mediated repression of FLO1. Altogether we demonstrate that 17-24 stretch is essential for the Tup1 independent binding of Cyc8 at the promoters assisted by Hda1, leading to the strong repression of FLO1 transcription. In the absence of the 17-24 stretch, Cyc8 cannot bind, resulting in uncontrolled transcription of FLO1.

molecular biology↗

bioRxiv2024

Topology-Driven Discovery of Transmembrane Protein S-Palmitoylation

Protein S-palmitoylation is a reversible lipophilic posttranslational modification regulating a diverse number of signaling pathways. Within transmembrane proteins (TMPs), S-palmitoylation is implicated in conditions from inflammatory disorders to respiratory viral infections. Many small-scale experiments have observed S-palmitoylation at juxtamembrane Cys residues. However, most large-scale S-palmitoyl discovery efforts rely on trypsin-based proteomics within which hydrophobic juxtamembrane regions are likely underrepresented. Machine learning- by virtue of its freedom from experimental constraints - is particularly well suited to address this discovery gap surrounding TMP S-palmitoylation. Utilizing a UniProt-derived feature set, a gradient boosted machine learning tool (TopoPalmTree) was constructed and applied to a holdout dataset of viral S-palmitoylated proteins. Upon application to the mouse TMP proteome, 1591 putative S-palmitoyl sites (i.e. not listed in SwissPalm or UniProt) were identified. Two lung-expressed S-palmitoyl candidates (synaptobrevin Vamp5 and water channel Aquaporin-5) were experimentally assessed. Finally, TopoPalmTree was used for rational design of an S-palmitoyl site on KDEL-Receptor 2. This readily interpretable model aligns the innumerable small-scale experiments observing juxtamembrane S-palmitoylation into a proteomic tool for TMP S-palmitoyl discovery and design, thus facilitating future investigations of this important modification.

molecular biology↗

bioRxiv2024

Wirelessly-Powered Ingestible Electronic Capsule for Non-invasive Gastrointestinal Optogenetics

Optogenetics enables the activation and inhibition of neurons with cell specificity. The gut harbors intricate networks of enteric and central neurons. Uncovering these neuronal pathways in vivo is challenging with traditional neuroscience probes due to the highly motile and harsh gut environment. Here we report the development of an ingestible electronic capsule for non-invasive optical gut stimulation (ICOPS) in rodents. ICOPS is powered wirelessly via a transmitter coil, dosed via oral gavage, and safely excreted without causing obstruction. ICOPS permits modular interchangeability of onboard light-emitting diodes (LEDs) for illumination. We exemplify this with optical irradiance at 470 nm, a commonly-used wavelength in optogenetics for activating channelrhodopsin2. ICOPS features a micro-LED ({micro}LED), a 460-turn coil wound around a ferrite core, and a resonating capacitor. We optimized the transmitting and receiving circuits to achieve maximum power transfer at low operating frequencies (45-140 kHz), overcoming challenges like loose coupling and misalignment. The capsule operates effectively at a distance up to 12 cm longitudinally, 9 cm laterally, and 75{degrees} rotational angle relative to the magnetic field. Specific absorption rate (SAR) calculations indicate transmitter-induced SAR levels within safe limits for the occupational environment at 6 Arms and 45 and 63 kHz frequencies ICOPS is robust and transits through the rat gastrointestinal (GI) tract in under 20 hours intact. We demonstrate in vivo functionality and viability of ICOPS using IVIS micro-computed tomography ({micro}CT). ICOPS could pave the way for non-invasive optogenetic interfacing of enteric neural circuits towards their use to regulate motility, visceral pain, and other gastrointestinal disorders.

bioengineering↗

bioRxiv2024

An alternative mechanism for activation of innate immune signaling by MDA5

Long double-stranded (ds) RNA in the cytosol acts as a potent inflammatory molecule recognized by the receptor MDA5, triggering the innate immune response. Mutations affecting MDA5 ATPase activity lead to severe pathological conditions. MDA5 nucleoprotein filament assembly-disassembly dynamic is proposed to regulate dsRNA recognition, though the exact mechanism remains unclear. Here, we employed magnetic tweezers to monitor the assembly and manipulate MDA5 filaments at the single dsRNA level. Following a slow nucleation event, MDA5 assembles cooperatively and directionally into (partial) filaments and utilizes ATP hydrolysis to compact dsRNA via single-stranded (ss) RNA loop extrusion, even against a significant opposing force. This compacted state is further stabilized by oligomerization of MDA5s caspase recruitment domain (CARD) and requires high force to be disrupted. ssRNA gaps impaired compaction, suggesting a new mechanism for dsRNA recognition. We propose that MDA5 mediated dsRNA compaction captures viral dsRNA, preventing further usage for viral replication.

biophysics↗

bioRxiv2024

Miniaturizing, Modifying, and Augmenting Nature's Proteins with Raygun

Proteins have evolved over billions of years through extensive and coordinated substitutions, insertions and deletions (indels). Computational protein design cannot yet fully mimic natures ability to engineer new proteins from existing templates. Protein language models generate informative per-residue representations, but leveraging them to execute large-scale, function-preserving mutations and indels has remained beyond reach. We introduce Raygun, a generative AI framework that unlocks efficient miniaturization, modification, and augmentation of proteins, using a novel probabilistic encoding of protein sequences constructed from language model embeddings. Emulating evolution, Raygun shrinks proteins by 10-25% (sometimes over 50%) while preserving predicted structural integrity and fidelity, introduces extensive sequence diversity while preserving functional sites, and can expand proteins beyond their natural size. These capabilities unlock new opportunities in gene therapy and biotechnology. In cell-based validation, Raygun successfully miniaturized fluorescent proteins, two of which are smaller than 96% of fluorescent proteins reported in FPbase, as well as TurboID, a synthetic biotin ligase widely adopted for proteomics. It also successfully expanded Epidermal Growth Factor (EGF), a natural binding partner to the EGFR protein, generating EGF variants with higher binding affinity than the wildtype. Rayguns conceptual innovations in template-based protein design reveal that protein function can be encoded in a length-independent space. This fundamental insight bridges protein representation learning with evolutionary biology and could unlock the development of more efficient molecular tools and biological therapeutics.

bioinformatics↗

bioRxiv2024

Efficient modular system identification provides a high-resolution assay of temporal processing and reveals the multilevel effects of attention along the human auditory pathway

Human studies of auditory temporal processing and the effects therein of aging, hearing loss, musicianship, and other auditory processing disorders have conventionally employed brainstem evoked potentials (e.g., FFRs/EFRs targeting specific modulation frequencies). Studies of temporal processing in forebrain structures are fewer and are often restricted to the 40 Hz steady-state response. One factor contributing to the limited investigation is the lack of a fast and reliable method to characterize temporal processing non-invasively in humans over a wide range of modulation frequencies. Here, we use a system-identification approach where white noise, modulated using an extended maximum-length sequence (em-seq), is employed to target stimulus energy toward a modulation-frequency range of interest and efficiently obtain a robust auditory modulation-temporal response function or mod-TRF. The mod-TRF can capture activity from sources in the early processing pathway (5-7 ms latency), middle-latency region (MLR), and late latency region (LLR). The mod-TRF is a high-resolution, modular assay of the temporal modulation transfer function (tMTF) in that the distinct neural components contributing to the tMTF can be separated on the basis of their latency, modulation frequency band, and scalp topography. This decomposition provides the insight that the seemingly random individual variation in the shape of the tMTF can be understood as arising from individual differences in the weighting and latency of similar underlying neural sources in the composite scalp response. We measured the mod-TRF under different states of attention and found a reduction in latency or enhancement of amplitude of the response from specific sources. Surprisingly, we found that attention effects can extend to the earliest parts of the processing pathway (5ms) in highly demanding tasks. Taken together, the mod-TRF is a promising tool for dissecting auditory temporal processing and obtain further insight into a variety of phenomenon such as aging, hearing loss, and neural pathology.

neuroscience↗

bioRxiv2024

Effects of short-term isolation on vocal and non-vocal social behaviors in prairie voles

Social isolation affects the brain and behavior in a variety of animals, including humans. Studies in traditional laboratory rodents, including mice and rats, have supported the idea that short-term social isolation promotes affiliative social behaviors, while long-term isolation promotes anti-social behaviors, including increased aggression. Whether the effects of isolation on the social behaviors of mice and rats generalize to other rodents remains understudied. In the current study, we characterized the effects of short-term (3-days) social isolation on the vocal and non-vocal social behaviors of adult prairie voles (Microtus ochrogaster) during same-sex and opposite-sex social interactions. Our experiments revealed that short-term isolation did not affect rates of ultrasonic vocalizations or time spent in non-aggressive social behaviors and huddling. In contrast, short-term isolation affected aggression in a sex- and context-specific manner during male-male interactions. Our findings highlight the importance of comparative work across species and the consideration of social context to understand the diverse ways in which social isolation can impact social behavior.

animal behavior and cognition↗

bioRxiv2024

Comparative analysis of the syncytiotrophoblast in placenta tissue and trophoblast organoids using snRNA sequencing

The outer surface of chorionic villi in the human placenta consists of a single multinucleated cell called the syncytiotrophoblast (STB). The unique cellular ultrastructure of the STB presents challenges in deciphering its gene expression signature at the single-cell level, as the STB contains billions of nuclei in a single cell. There are many gaps in understanding the molecular mechanisms and developmental trajectories involved in STB formation and differentiation. To identify the underlying control of the STB, we performed comparative single nucleus (SN) and single cell (SC) RNA sequencing on placental tissue and tissue-derived trophoblast organoids (TOs). We found that SN RNA sequencing was essential to capture the STB population from both tissue and TOs. Differential gene expression and pseudotime analysis of TO-derived STB identified three distinct nuclear subtypes reminiscent of those recently identified in vivo. These included a juvenile nuclear population that exhibited both CTB and STB marker expression, a population enriched in genes involved in oxygen sensing, and finally a subtype enriched in transport and GTPase signaling molecules. Notably, suspension culture conditions of TOs that restore the native orientation of the STB (STBout) showed elevated expression of canonical STB markers and pregnancy hormones, along with a greater proportion of the STB nucleus subtype specializing in transport and GTPase signaling, compared to those cultivated with an inverted STB polarity (STBin). Gene regulatory analysis identified novel markers of STB differentiation conserved in tissue and TOs, including the chromatin remodeler RYBP, that exhibited STB-specific RNA and protein expression. CRISPR/Cas9 knockout of RYBP in STBin TOs did not impact cell-cell fusion; however, bulk RNA sequencing revealed downregulation of the pregnancy hormone CSH1 and upregulation of multiple genes associated with the oxygen-sensing STB nuclear subtype. Finally, we compared STB gene expression signatures amongst first trimester tissue, full-term tissue, and TOs, identifying many commonalities but also notable variability across each sample type. This indicates that STB gene expression is responsive to its environmental context. Our findings emphasize the utility of TOs to accurately model STB differentiation and the distinct nuclear subtypes observed in vivo, offering a versatile platform for unraveling the molecular mechanisms governing STB functions in placental biology and disease.

developmental biology↗