Biology subjects

Rao, J.

Publications and source records attributed to Rao, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Endothelial Cu Uptake Transporter CTR1 Senses Disturbed Flow to Promote Atherosclerosis through Cuproptosis

Endothelial cells (ECs) lining blood vessels sense disturbed blood flow (D-flow), which drives mitochondrial dysfunction and atherosclerosis. Copper (Cu) is an essential micronutrient, and its disruption of homeostasis has been implicated in atherosclerosis. Cellular Cu levels are tightly controlled by Cu transport proteins including the Cu importer CTR1. Cuproptosis is a recently discovered form of regulated cell death triggered by mitochondrial Cu accumulation, but its endogenous stimulants and role in atherosclerosis remain unknown. Using EC-specific CTR1-deficient mice and cultured ECs, we show that endothelial CTR1 responds to D-flow by increasing mitochondrial Cu levels through its interaction with the mitochondrial Cu transporter SLC25A3 at caveolae/lipid rafts. This leads to the aggregation of lipoylated mitochondrial proteins, mitochondrial dysfunction, and cuproptosis, thereby exacerbating atherosclerosis. Importantly, mitochondria-targeted Cu-chelating nanoparticles effectively mitigate D-flow-induced cuproptosis and atherosclerosis, highlighting the endothelial CTR1-SLC25A3-mitochondrial Cu axis as a potential therapeutic target.

cell biology↗

bioRxiv2025

Rosace-AA: Enhancing Interpretation of Deep Mutational Scanning Data with Amino Acid Substitution and Position-Specific Insights

Proteins are dynamic systems whose function and behavior are sensitive to environmental conditions and often involve multiple cellular roles. Deep mutational scanning (DMS) experiments generate extensive datasets to capture the functional consequences of mutations. However, the sheer volume of data presents challenges in visualization and interpretation. Current approaches often rely on heatmaps, but these methods fail to capture the nuanced effects of amino acid (AA) substitutions, which are essential for understanding mutational impact. To address this, we extend the Rosace framework with Rosace-AA, a model that incorporates both position-specific information and AA substitution trends. Using substitution matrices like BLOSUM90, Rosace-AA offers a flexible and interpretable approach to summarize DMS data oil both protein-level and position-level. We demonstrate its utility across datasets, including OCTI and MET kinase, showing that Rosace-AA highlights key positions where mutations deviate from expected substitution patterns and captures functionally relevant variation in protein behavior across multiple DMS screens. These results suggest that Rosace-AA enables more robust and interpretable analysis of complex DMS datasets.

bioinformatics↗

bioRxiv2024

Base-modified nucleotides mediate immune signaling in bacteria

Signaling from pathogen sensing to effector activation is a fundamental principle of cellular immunity. While cyclic (oligo)nucleotides have emerged as key signaling molecules, the existence of other messengers remains largely unexplored. Here, we reveal a bacterial anti-phage system that mediates immune signaling through nucleobase modification. Immunity is triggered by phage nucleotide kinases, which, combined with the system-encoded adenosine deaminase, produce deoxyinosine 5-triphosphate (dITP) as immune messengers. The dITP signal activates downstream effector to mediate cellular NAD+ depletion, resulting in population-level defense through the death of infected cells. To counteract immune signaling, phages deploy specialized enzymes that deplete cellular dAMP, the precursor of dITP messengers. Our findings uncover a nucleobase modification-based anti-phage signaling pathway, establishing noncanonical nucleotides as a new type of immune messengers in bacteria.

microbiology↗

bioRxiv2024

EnzymeCAGE: A Geometric Foundation Model for Enzyme Retrieval with Evolutionary Insights

Enzyme catalysis is fundamental to life, driving the chemical transformations that sustain biological processes and support industrial applications. However, unraveling the intertwined relationships between enzymes and their catalytic reactions remains a significant challenge. Here, we present EnzymeCAGE, a catalytic-specific geometric foundation model trained on approximately 1 million structure-informed enzyme-reaction pairs, spanning over 2,000 species and encompassing an extensive diversity of genomic and metabolic information. EnzymeCAGE features a geometry-aware multi-modal architecture coupled with an evolutionary information integration module, enabling it to effectively model the nuanced relationships between enzyme structure, catalytic function, and reaction specificity. EnzymeCAGE supports both experimental and predicted enzyme structures and is applicable across diverse enzyme families, accommodating a broad range of metabolites and reaction types. Extensive evaluations demonstrate EnzymeCAGEs state-of-the-art performance in enzyme function prediction, reaction de-orphaning, catalytic site identification, and biosynthetic pathway reconstruction. These results highlight its potential as a transformative foundation model for understanding enzyme catalysis and accelerating the discovery of novel biocatalysts.

synthetic biology↗

bioRxiv2024

Performance evaluation of structural variation detection using DNBSEQ whole-genome sequencing

DNBSEQ platforms have been widely used for variation detection, including single-nucleotide variants and short insertions and deletions, comparable to Illumina. However, the performance of structural variations (SVs) detection using DNBSEQ platforms remains unclear. We assessed SV detection using 40 tools on eight DNBSEQ and two Illumina NA12878 whole-genome sequencing datasets. We confirmed that the performance of SV detection on DNBSEQ and Illumina platforms is consistent when using the same tools, with high consistency in metrics of number, size, precision, and sensitivity. Furthermore, we integrated representative SV sets for both DNBSEQ (4,785 SVs) and Illumina (6,797 SVs) platforms. We found high consistency between the DNBSEQ and Illumina SV sets in terms of genomic characteristics, including repetitive regions, GC distribution, difficult-to-sequence regions, and gene features, indicating the value of both platforms in understanding repetitive regions and the genomic context of SVs. Our study provides a benchmark resource for further studies of SVs using DNBSEQ platforms.

genomics↗

bioRxiv2024

In vivo imaging of T-cell coregulator B7-H4 reveals protumor macrophage status in prostate cancer

BackgroundB7-H4 is a cell surface ligand overexpressed by tumors to inhibit T cell functions and evade the immune system. B7-H4 is minimally expressed in normal tissues but is highly expressed by various cancer cells and tumor-associated macrophages (TAM). Despite its importance as an immune checkpoint inhibitor, no imaging techniques specifically targeting B7-H4 have been established. To close this gap, we sought to assess the ability of a monoclonal antibody (mAb) based immunoPET radiotracer to visualize B7-H4 in human and murine prostate cancer models. MethodsAnti-B7-H4 mAb clone 2H9 was functionally characterized for binding to the human and mouse B7-H4 protein. The antibody was conjugated with chelator p-SCN-Bn-Deferoxamine (DFO) and labeled with radioisotope Zirconium-89 (89Zr) to obtain immunoPET tracer 89Zr-2H9-mAb. The biolayer interferometry method was used to test the binding kinetics of DFO-2H9-mAb compared to that of parental 2H9 mAb. A group of six athymic nude mice with human DU145 prostate tumor xenograft underwent MicroPET imaging after tail vein injection of [~]150{micro}Ci 89Zr-2H9-mAb or non-binding 89Zr-Isotype-mAb. Next, immunocompetent C57BL/6J mice with TRAMP-C2 tumors each were injected with either PBS (n=8), cold 2H9 mAb (10mg/kg) to block B7-H4 (n=6), or chlodronate liposome (15mg/kg) to cause total macrophage depletion (n=6), followed by 89Zr-2H9-mAb MicroPET imaging. An ex vivo biodistribution assay was performed after 144 hr post radiotracer injection. Tumor radiotracer binding, quantified as a percentage injected dose per gram (%ID/g), was compared between different experimental groups using two-way ANOVA with Bonferroni or Tukey corrections. ResultsImmunoconjugation yielded a 2.59 {+/-} 0.08 chelator-to-antibody ratio, and the binding of DFO conjugated 2H9-mAb was similar to that of parental 2H9 mAb, with unaffected affinity in targeting B7-H4 protein moiety. The radiochemical purity of 89Zr-2H9-mAb tracer was yielded >95% with an average specific activity of 5{micro}Ci/{micro}g antibody. DU145 tumor xenografts demonstrated significantly stronger radiotracer binding at 24, 48, 72, 96, and 120 hr than the non-binding isotype control group. In TRAMP-C2 tumor xenografts, the radiotracer binding in B7-H4 blocked tumors was significantly lower than in the non-blocked PBS-injected group. Macrophage depletion resulted in a significant decrease in tumor binding compared to the control group. 89Zr-2H9-mAb could efficiently distinguish tumors with high sensitivity, showing a high correlation between PET imaging and bio-distribution. More importantly, the immunohistochemistry of the harvested tumor revealed no significant difference between the three groups, as discernible through in vivo PET imaging. ConclusionThis study highlights the potential of B7-H4 immunoPET imaging for monitoring immunotherapy response. With the emerging potential of B7-H4 blocking as an immunotherapeutic, immunoPET imaging could be readily expanded to patient stratification and therapy monitoring. B7-H4 imaging could augment our understanding of B7-H4 dynamics in response to various therapeutic interventions in clinical trials. The new B7-H4 immunoPET probe is, in principle, clinically translatable.

immunology↗

bioRxiv2024

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of [~]5,764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I [1/2] inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

cancer biology↗

bioRxiv2024

AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactions

AlphaFold3 has set the new state-of-the-art in predicting protein-protein complex structures. However, the complete picture of biomolecular interactions cannot be fully captured by static structures alone. In the field of protein engineering and antibody discovery, the connection from structure to function is often mediated by binding energy. This work benchmarks AlphaFold3 against SKEMPI, a commonly used binding energy dataset. We demonstrate that AlphaFold3 learns unique information and synergizes with force field, profile-based, and other deep learning methods in predicting the mutational effects on protein-protein interactions. We hypothesize that AlphaFold3 captures a more global effect of mutations by learning a smoother energy landscape, but it lacks the modeling of full atomic details that are better addressed by force field methods, which possess a more rugged energy landscape. Integrating both approaches could be an interesting future direction. All of our benchmark results are openly available at https://github.com/luwei0917/AlphaFold3_PPI.

biophysics↗

bioRxiv2024

Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN), and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model.

bioinformatics↗

bioRxiv2023

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) enables functional insight into protein mutations with multiplexed measurements of thousands of genetic variants in a protein simultaneously. The small sample size of DMS renders classical statistical methods ineffective, for example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based deep mutational scanning data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. To benchmark Rosace against existing methods, we developed Rosette, a simulation framework that simulates the distributional properties of DMS. Further, we show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools under Rosette simulation and real data.

bioinformatics↗

bioRxiv2023

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study

ObjectiveThe intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group. MethodsA state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in i) all epilepsies; ii) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS); iii) non-lesional temporal lobe epilepsy (TLE-NL); iv) genetic generalised epilepsy; and (v) extra-temporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. ResultsAcross all epilepsies, reduced total cerebellar volume was observed (d=0.42). Maximum volume loss was observed in the corpus medullare (dmax=0.49) and posterior lobe grey matter regions, including bilateral lobules VIIB (dmax= 0.47), Crus I/II (dmax= 0.39), VIIIA (dmax=0.45) and VIIIB (dmax=0.40). Earlier age at seizure onset ({eta}{rho}2max=0.05) and longer epilepsy duration ({eta}{rho}2max=0.06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. SignificanceWe provide robust evidence of deep cerebellar and posterior lobe subregional grey matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in non-motor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellum subregions into neurobiological models of epilepsy. Key pointsO_LICerebellar involvement in epilepsy is poorly understood within current cortico-centric models of this disease C_LIO_LIWe used a novel, deep learning segmentation tool to parcellate the cerebellum into 28 anatomical subunits using an international MRI dataset of 1,602 individuals with epilepsy (aged between 18 and 65 years old) including temporal lobe epilepsy with hippocampal sclerosis (TLE-HS, n=562), TLE non-lesional (TLE-NL, n=284), generalised genetic epilepsy (GGE, n=186) and extra temporal focal epilepsy (ETLE, n=251) and 1,022 controls. C_LIO_LIAcross all epilepsies (vs. controls) robust changes in the corpus medullare and posterior lobe "non-motor" regions were observed, with maximal differences in bilateral VIIB and Crus II lobules. Lower volume of these regions correlated with longer disease duration. Anterior "motor lobe" regions were relatively spared. C_LIO_LIFindings were most pronounced in TLE-HS and ETLE groups, with distinct neuroanatomical profiles observed. C_LIO_LICortical thinning was associated with pronounced cerebellar volume loss in TLE-HS epilepsy, relative to controls. C_LI ETHICAL PUBLICATION STATEMENTWe confirm that we have read the Journals position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

neuroscience↗

bioRxiv2023

Predicting Disease-gene Associations through Self-supervised Mutual Infomax Graph Convolution Network

Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contributes to treatments, but a large number of associations still remained unexplored. To identify novel disease-gene association, many computational methods have been developed using disease and gene related prior knowledge. However, these methods remain of relatively inferior performance due to the limited external data sources and the inevitable noise among the prior knowledge. In this study, we have developed a new method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), to predict disease-gene associations under the guidance of external disease-disease and gene-gene collaborative graphs. The noises within the collaborative graphs were eliminated by maximizing the mutual information between nodes and neighbors through a graphical mutual infomax layer. In parallel, the node interactions were strengthened by a novel informative message passing layer to improve the learning ability of graph neural network. The extensive experiments showed that our model achieves performance improvement over the state-of-art method by more than 8% on AUC. The datasets, source codes and trained models of MiGCN are available at https://github.com/biomed-AI/MiGCN.

bioinformatics↗

bioRxiv2023

Artificial intelligence-driven morphology-based enrichment of malignant cells from body fluid

Cell morphology is a fundamental feature used to evaluate patient specimens in pathological analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with high background of non-malignant cells, restricting the ability for downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multi-dimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low-levels or undetectable in pre-sort patient samples. Combined, our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multi-dimensional morphology analysis, and microfluidic sorting.

pathology↗

bioRxiv2022

Highly Excretable Gold Supraclusters for Translatable In Vivo Raman Imaging of Tumors

Raman spectroscopy provides excellent specificity for in vivo preclinical imaging through a readout of fingerprint-like spectra. To achieve sufficient sensitivity for in vivo Raman imaging, metallic gold nanoparticles larger than 10 nm were employed to amplify Raman signals via surface-enhanced Raman scattering (SERS). However, the inability to excrete such large gold nanoparticles has restricted the translation of Raman imaging. Here we present Raman-active metallic gold supraclusters that are biodegradable and excretable as nanoclusters. Although the small size of the gold nanocluster building blocks compromises the electromagnetic field enhancement effect, the supraclusters exhibit bright and prominent Raman scattering comparable to that of large gold nanoparticle-based SERS nanotags due to high loading of NIR-resonant Raman dyes and much suppressed fluorescence background by metallic supraclusters. The bright Raman scattering of the supraclusters was pH-responsive, and we successfully performed in vivo Raman imaging of acidic tumors in mice. Furthermore, in contrast to large gold nanoparticles that remain in the liver and spleen, the supraclusters dissociated into small nanoclusters, and 73% of the administered dose to mice was excreted over 4 months. The highly excretable Raman supraclusters demonstrated here offer great potential for clinical applications of in vivo Raman imaging by replacing non-excretable large gold nanoparticles.

bioengineering↗

bioRxiv2022

Cross-modal Graph Contrastive Learning with Cellular Images

Constructing discriminative representations of molecules lies at the core of a number of domains such as drug discovery, material science, and chemistry. State-of-the-art methods employ graph neural networks (GNNs) and self-supervised learning (SSL) to learn the structural representations from unlabeled data, which can then be fine-tuned for downstream tasks. Albeit powerful, these methods that are pre-trained solely on molecular structures cannot generalize well to the tasks involved in intricate biological processes. To cope with this challenge, we propose using high-content cell microscopy images to assist in learning molecular representation. The fundamental rationale of our method is to leverage the correspondence between molecular topological structures and the caused perturbations at the phenotypic level. By including cross-modal pre-training with different types of contrastive loss functions in a unified framework, our model can efficiently learn generic and informative representations from cellular images, which are complementary to molecular structures. Empirical experiments demonstrated that the model transfers non-trivially to a variety of downstream tasks and is often competitive with the existing SSL baselines, e.g., a 15.4% absolute Hit@10 gains in graph-image retrieval task and a 4.0% absolute AUC improvements in clinical outcome predictions. Further zero-shot case studies show the potential of the approach to be applied to real-world drug discovery.

bioinformatics↗

bioRxiv2022

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

Illuminating interactions between proteins and small drug molecules is a longstanding challenge in the field of drug discovery. Despite the importance of understanding these interactions, most previous works are limited by hand-designed scoring functions and insufficient conformation sampling. The recently-proposed graph neural network-based methods provides alternatives to predict protein-ligand complex conformation in a one-shot manner. However, these methods neglect the geometric constraints of the complex structure and weaken the role of local functional regions. As a result, they might produce unreasonable conformations for challenging targets and generalize poorly to novel proteins. In this paper, we propose Trigonometry-Aware Neural networKs for binding structure prediction, TANKBind, that builds trigonometry constraint as a vigorous inductive bias into the model and explicitly attends to all possible binding sites for each protein by segmenting the whole protein into functional blocks. We construct novel contrastive losses with local region negative sampling to jointly optimize the binding interaction and affinity. Extensive experiments show substantial performance gains in comparison to state-of-the-art physics-based and deep learning-based methods on commonly-used benchmark datasets for both binding structure and affinity predictions with variant settings.

biophysics↗

bioRxiv2022

Reconstructing human Brown Fat developmental trajectory in vitro

Brown adipocytes represent a specialized type of mammalian adipocytes able to uncouple nutrient catabolism from ATP generation to dissipate energy as heat. They play an important role in mammals, allowing non-shivering thermogenesis to regulate body temperature in response to cold exposure. In humans, the brown fat tissue is composed of small discrete depots found mostly throughout the neck and trunk region. Increasing brown fat activity either with drug treatment or cell therapy is considered a potential approach for the treatment of metabolic syndrome and obesity. The recent development of in vitro differentiation strategies relying on human pluripotent stem cells (hPSCs) offers the possibility to produce unlimited amounts of brown adipocytes. A strategy efficiently applied to several tissues is to recapitulate step by step the development of the tissue of interest by exposing hPSCs to the signaling cues used during normal embryonic development. However, this strategy has proven difficult to implement for brown fat as the development of this tissue is poorly understood. Here, we first used single cell RNA sequencing to characterize the development of interscapular brown fat in mouse. Our analysis identified a previously unrecognized population of brown adipocytes precursors characterized by expression of the transcription factor GATA6. We show that this precursor population can be efficiently generated from paraxial mesoderm precursors differentiated in vitro from hPSCs by modulating the signaling pathways identified in our transcriptomic analysis. These precursors can in turn be efficiently converted into functional brown adipocytes which can respond to adrenergic stimuli by increasing their metabolism resulting in heat production.

developmental biology↗

bioRxiv2022

Reconstruction and deconstruction of human somitogenesis in vitro

The body of vertebrates displays a segmental organization which is most conspicuous in the periodic organization of the vertebral column and peripheral nerves. This metameric organization is first implemented when somites, which contain the precursors of skeletal muscles and vertebrae, are rhythmically generated from the presomitic mesoderm (PSM). Somites then become subdivided into anterior and posterior compartments essential for vertebral formation and segmental patterning of the peripheral nervous system1-4. How this key somitic subdivision is established remains poorly understood. Here we introduce novel tridimensional culture systems of human pluripotent stem cells (PSCs), called Somitoids and Segmentoids, which can recapitulate the formation of epithelial somite-like structures with antero-posterior (AP) identity. Using these systems, we identified a key organizing function of the segmentation clock in converting temporal rhythmicity into the spatial regularity of anterior and posterior somitic compartments. We show that an initial salt-and-pepper expression pattern of the segmentation gene MESP2 in the newly formed segment is transformed into defined compartments of anterior and posterior identity via an active cell sorting mechanism. Moreover, we demonstrate a large degree of independence of the various patterning modules involved in somitogenesis including the segmentation clock, somite epithelialization and AP polarity patterning. Together we put forward a novel framework accounting for the symmetry breaking process initiating somite polarity patterning. Our work provides a valuable platform to decode general principles of somitogenesis and advance knowledge of human development.

developmental biology↗