Biology subjects

Choi, J.

Publications and source records attributed to Choi, J..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Combinatorial effects of Zingiber officinale and Citrus limon juices: Hypolipidemic and antioxidant insights from in vivo, in vitro, and in silico investigations

The combined use of Zingiber officinale (ginger) and Citrus limon (lemon) has long been valued in traditional medicine for their significant therapeutic properties, particularly in supporting cardiovascular health. However, their synergistic effects on hyperlipidemia and oxidative stress, both critical risk factors for cardiovascular diseases, remain underexplored. This study employed a multidisciplinary approach including in vivo (mice model with triton WR-1339-induced hyperlipidemia), in vitro (antioxidant assays such as DPPH and ABTS), and in silico (molecular docking targeting HMG-CoA reductase) to evaluate the hypolipidemic and antioxidant potential of ginger and lemon juices. Phytochemical analysis identified high concentrations of bioactive compounds, with ginger juice containing 6-gingerol (15.54% peak area) and lemon juice featuring hesperidin (13.85% peak area) and rutin (5.57% peak area). These compounds were instrumental in the observed pharmacological effects. The combined formulation significantly improved lipid profiles, reducing total cholesterol by 54.3%, triglycerides by 49.8%, and LDL by 58.1% while increasing HDL by 47.6% compared to hyperlipidemic controls (p < 0.05). Antioxidant assays revealed high free radical scavenging activity, with IC50 values of 30.92 {+/-} 2.00 {micro}g/mL for ABTS and 44.94 {+/-} 1.02 {micro}g/mL for DPPH in the combined formulation. Molecular docking confirmed strong binding affinities, with hesperidin and 6-gingerol showing binding energies of -10.4 kcal/mol and -9.6 kcal/mol, respectively, against HMG-CoA reductase. In conclusion, combining Zingiber officinale and Citrus limon juices demonstrates robust hypolipidemic and antioxidant effects, significantly outperforming the individual juices. These results support their potential as natural therapeutic agents for managing hyperlipidemia and oxidative stress. Further clinical studies are recommended to validate these promising findings and explore their application in nutraceutical development.

pharmacology and toxicology↗

bioRxiv2025

Aseptic, semi-sealed cranial chamber implants for chronic multi-channel neurochemical and electrophysiological neural recording in nonhuman primates

We developed an implantable neural interface for monitoring both chemical and electrical forms of brain activity in monkeys that maintains aseptic properties for year-long periods while leveraging the modular functions (e.g., sensor moveability) provided by a chamber system. Invasive electrophysiological recordings, especially in subcortical structures from nonhuman primates usually involves implanting electrodes into the brain through a skull-mounted chamber. These electrodes may be attached temporarily for several hours of recording, or permanently. Permanent attachments are favorable to allow for sealing the chamber completely from externally originating pathogenic species that can infiltrate and compromise the health of the animal. A sealed chamber also reduces the need for frequent chamber cleaning required to minimize the accumulation of pathogenic organisms. However, neurochemical measurements require specialized electrodes with extremely fragile carbon fiber tips and are not compatible with recently developed sealed chamber systems. Here, we leveraged osseointegrating materials and hermetic sealing strategies to enable both neurochemical and electrical neural activity measurements from a sealed chamber with an aspirating port for culturing chamber fluid to ensure an aseptic environment. The system was shown to provide successful recordings of neural activity in two monkeys while maintaining negative bacteria culture results for over a year post-implant.

neuroscience↗

bioRxiv2025

A lateralized pathway for associating nutrients with flavors

Animals learn about the external world, in part, via interoceptive signals1,2. For example, the nutrient content of food is first estimated in the mouth, in the form of flavor, and then measured again via slower signals from the gut. How these signals from the mouth and gut are integrated to drive learning is unknown. Here we identify a lateralized dopamine pathway that is specialized for learning about the nutrient content of food. We show that dopamine neurons in the ventral tegmental area (VTADA) are necessary for associating nutrients with flavors, and that post-ingestive nutrients trigger DA release selectively in a small region of the anterior basolateral amygdala (BLA) but not canonical DA targets in striatum. Remarkably, this nutrient-triggered DA release occurs preferentially on the left side of the brain in both mice and humans, revealing that the DA system is functionally lateralized. We identify the gut sensors that are responsible for nutrient-triggered DA release; show that they activate BLA-projecting DA neurons defined by expression of cholecystokinin (CCK); and demonstrate that stimulation of DA axon terminals in the anterior BLA drives flavor-nutrient learning but not other aspects of feeding behavior. Two-photon imaging of neurons in the left anterior BLA reveals that they integrate gustatory and post-ingestive cues, and silencing these neurons prevents flavor-nutrient learning. These findings establish a neural basis for how animals learn about the nutrient content of their food. They also reveal unexpectedly that post-ingestive nutrients are differentially represented on the right and left sides of the brain.

neuroscience↗

bioRxiv2025

{Phi}-Space ST: a platform-agnostic method to identify cell states inspatial transcriptomics studies

We introduce {Phi}-Space ST, a platform-agnostic method to identify continuous cell states in spatial transcriptomics (ST) data using multiple scRNA-seq references. For ST with supercellular resolution, {Phi}-Space ST achieves interpretable cell type deconvolution with significantly faster computation. For subcellular resolution, {Phi}-Space ST annotates cell states without cell segmentation, leading to highly insightful spatial niche identification. {Phi}-Space ST harmonises annotations derived from multiple scRNA-seq references, and provides interpretable characterisations of disease cell states by leveraging healthy references. We validate {Phi}-Space ST in three case studies involving CosMx, Visium and Stereo-seq platforms for various cancer tissues. Our method revealed niche-specific enriched cell types and distinct cell type co-presence patterns that distinguish tumour from non-tumour tissue regions. These findings highlight the potential of {Phi}-Space ST as a robust and scalable tool for ST data analysis for understanding complex tissues and pathologies.

bioinformatics↗

bioRxiv2025

Adaptive selection of p53 mutation metaplastic phenotypes in estrogen-independent progression of ER+ tumors: A mechanism for acquired resistance to hormonal therapy.

Estrogen receptor positive (ER+) subtypes of mammary adenocarcinoma comprise 79% of all breast cancer diagnosis and 67% of all breast cancer mortality. The paucity of models of ER+ mammary cancer that mimic human disease and response to treatment has limited critical preclinical study of mechanisms and new therapies for ER+ breast cancer. The Stat1 knockout, 129S6/SvEvTac-Stat1tm1Rds (Stat1-/-), females develop luminal type FoxA1+, ER+, and PR+ mammary carcinomas after prolonged latencies. Initial studies showed that a cell line derived from a Stat1-/- mammary carcinoma was tumorigenic in syngeneic mice, but non-tumorigenic in ovariectomized (Ovx) mice. Here, data shows that Ovx performed after SSM2 tumors establish growth results in ovarian hormone independent growth. The viable post-Ovx tumors were primarily composed of metaplastic CK14+ basal type cells with a high percentage p53 immunohistochemistry (IHC) positive "mutation pattern", rather than the original luminal type tumors with low percent "wild type" pattern p53. Comparing whole exome sequences of ER+ Stat1-/- mammary tumors before and after Ovx, revealed basal keratins, mesenchymal (EMT) phenotypes, and unique mutation profiles in genes, including Trp53 and Prlr, in the estrogen-independent tumors. Our experimental findings are consistent with the clinical evidence of tumor heterogeneity of ER+ breast cancers in patients in recent whole genome sequencing studies. Similarly, spontaneous Stat1-/- tumors with high percentage p53 "mutation pattern" were more basaloid and grew rapidly after Ovx, while retaining high expression of ER and FoxA1. This study demonstrates that the STAT1-/-, ER+ estrogen dependent breast cancers can become resistant to through clonal selection of mammary cells comprised of metaplastic p53+/CK14+ basaloid cells.

cancer biology↗

bioRxiv2025

Micro-invasive probes for monitoring electrical and chemical neural activity in nonhuman primates

We leveraged carbon fiber materials for creating sensors that provide dual neurochemical and electrical neural activity recording at micro-invasive (10 {micro}m) spatial footprints proximal to recording sites, and enabling these measurements from deep brain targets of primates with conventional cranial chambers. These shaft-assisted micro-invasive probes (s-{micro}IPs) are approximately 10 {micro}m in diameter along the distal length (1 - 15 mm) immediately surrounding the targeted recording site. This micro-invasive portion ensures that the recording site is isolated from tissue damage induced by the wider shaft portion of the device. The shaft (150 - 165 {micro}m in diameter) within the device stiffens the remaining length of the probe (> 100 mm), and provides compatibility with standard intracranial insertion protocols (e.g., guide tubes and chamber setups) that require a sufficiently rigid and long shaft for deep brain insertion in monkeys. The s-{micro}IP was further expanded to provide dual-channel chemical and electrical neural activity recording with micrometer spatial resolution. Measurements of reward- and movement-related dopamine, spikes, and local field potentials were made from single and dual-channel s-{micro}IPs implanted in task-performing monkeys. Recordings from chronically implanted s-{micro}IPs display the capability of functional multi-modal (chemical and electrical) neural activity measurements over 1-year post-implantation from micro-invasive devices.

bioengineering↗

bioRxiv2025

Diet links gut chemistry with cancer risk in C57Bl/6 mice and human colorectal cancer patients

The gastrointestinal tract is a complex ecosystem in which host tissues, microbial communities, and dietary inputs interact to shape metabolic outputs and epithelial homeostasis. Western-style diets, characterized by high fat and protein and low micronutrient content, represent a sustained ecological perturbation linked to colorectal cancer (CRC), yet specific mechanisms that impact risk remain poorly defined. Here, using a purified Western-style diet (NWD1) that induces sporadic intestinal and colon tumors in wild-type C57BL/6 mice we demonstrate how chronic dietary disturbance restructures gut community composition and sulfur metabolism, and how these changes modulate intestinal stem cell responses. Mice fed NWD1 for 24 weeks exhibited consistent shifts in ecosystem function, including a threefold increase in fecal sulfide production (P < 0.00001) and expansion of Erysipelotrichaceae family taxa. This altered chemical landscape associates with increased expression of mitochondrial sulfide oxidation pathways in Lgr5hi intestinal stem cells. Meta-analysis of human CRC cohorts revealed concordant enrichment of Erysipelotrichaceae, alongside established CRC-associated taxa such as Solobacterium moorei, indicating conserved ecological signatures across systems. Together, these findings support a model in which the Western-style diet drives a persistent shift in gut ecosystem structure and function toward a sulfide producing state that challenges epithelial homeostasis, with conserved microbial and metabolic configurations emerging prior to overt disease in mouse models.

cancer biology↗

bioRxiv2025

scMILD: Single-cell Multiple Instance Learning for Sample Classification and Associated Subpopulation Discovery

Linking cellular states to clinical phenotypes is a major challenge in single-cell analysis. Here, we present scMILD, a weakly supervised Multiple Instance Learning framework that robustly identifies condition-associated cells using only sample-level labels. After systematically validating scMILDs accuracy through controlled simulations, we applied it to diverse disease datasets, confirming its ability to retrieve known biological signatures. Building on this, our sample-informed analysis of scMILD-identified monocytes in COVID-19 revealed a temporal transition from an early antiviral to a late stress-response state. Furthermore, in a novel cross-disease application, a model trained on COVID-19 successfully stratified Lupus patients and distinguished shared inflammatory states from disease-specific ones. scMILD thus provides a validated and versatile strategy to dissect cellular heterogeneity, bridging single-cell observations with high-level phenotypes.

bioinformatics↗

bioRxiv2025

APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this unique, comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8+ T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

immunology↗

bioRxiv2024

Adherent-suspension plasticity promotes the dissemination and colonization of circulating tumor cells

Cell competition within the primary tumor drives tumor growth by promoting the uncontrolled proliferation of winner cells and eliminating loser cells by sensing cell fitness. However, the mechanism of how cell competition confers the loser cells with the metastatic potential of circulating tumor cells (CTCs) to transit between dissemination and colonization remains elusive. Here we found cell competition gives rise to less fit but viable cells that transformed into CTCs via adherent-to-suspension transition (AST) mechanisms. The induction of hematopoietic transcription factors hijacked by solid tumor cells rendered CTCs competent to reprogram their anchorage dependency and disseminate into the bloodstream, while subsequent suppression of these factors was critical to regain adhesion and colonize metastatic lesions. Disrupting the oscillatory dynamics of AST factors blocked the adherent-suspension plasticity (ASP) of breast cancer CTCs and suppressed lung metastasis. Furthermore, multiregional single-cell transcriptomic analyses of matched primary tumors, CTCs, and metastatic lesions from de novo metastatic breast cancer patients demonstrate the critical role of ASP in metastasis. SignificanceWe demonstrate cell competition-mediated displacement of loser cells manifests dynamic oscillation of AST factors that confer anchorage plasticity to circulating tumor cells critical for their dissemination and colonization in metastasis. These findings highlight the potential of targeting AST factors to develop effective anti-metastatic therapies.

cancer biology↗

bioRxiv2024

Type I interferon signaling enhances kainic acid-induced seizure severity

Epilepsy is a chronic neurological disorder characterized by recurrent seizures, yet the role and mechanisms of type I interferon (IFN) signaling in seizure conditions remain elusive. In this study, we demonstrate that type I IFN signaling exacerbates seizure phenotypes in a kainic acid-induced seizure mouse model. We found that the absence of type I IFN signaling in Ifnar1-/- mice led to decreased neuronal excitability and microglial activation in response to kainic acid stimulation. Conversely, intracerebroventricular injection of IFN-{beta} heightened the severity of kainic acid-induced seizures. In vitro calcium imaging revealed that IFN-{beta} treatment amplified both basal and kainic acid-induced neuronal excitability, though no significant difference was observed in basal neuronal excitability between wild-type and Ifnar1-/- neurons. Furthermore, Ifnar1-/- mice exhibited reduced mTOR activation in the brain following kainic acid administration. Consistent with this finding, IFN-{beta} treatment induced mTOR activation, as indicated by S6 phosphorylation in in vitro mixed glial cultures. Taken together, these results demonstrate the critical role of type I IFN signaling in seizure pathogenesis and suggest that targeting type I IFNs could be a promising therapeutic strategy for reducing seizure severity and mitigating epilepsy.

neuroscience↗

bioRxiv2024

Multi-omics analysis in mouse primary cortical neurons reveals complex positive and negative biological interactions between constituent compounds in Centella asiatica

BackgroundA water extract of the Ayurvedic plant Centella asiatica (CAW) improves cognitive function in mouse models of aging and Alzheimers disease, and affects dendritic arborization, mitochondrial activity and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW although little is known about how interactions between these compounds contribute to the plants therapeutic benefit. MethodsMouse primary cortical neurons were treated with CAW, or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA). Treatment effects on the cell transcriptome (18,491 genes) and metabolome (192 metabolites) relative to vehicle control were evaluated using RNAseq and metabolomic analyses respectively. ResultsExtensive differentially expressed genes (DEGs) were seen with all treatments, as well as evidence of interactions between compounds. Notably many DEGs seen with TT treatment were not observed in the TTCQA condition, possibly suggesting CQA reduced the effects of TT. Moreover, additional gene activity seen with CAW as compared to TTCQA indicate the presence of additional compounds in CAW that further modulate TTCQA interactions. Weighted Gene Correlation Network Analysis (WGCNA) identified 4 gene co-expression modules altered by treatments that were associated with extracellular matrix organization, fatty acid metabolism, cellular response to stress and stimuli, and immune function. Compound interaction patterns were seen at the eigengene level in these modules. Interestingly, in metabolomics analysis, the TTCQA treatment saw the highest number of changes in individual metabolites (20), followed by CQA (15), then TT (8) and finally CAW (3). WGCNA analysis found two metabolomics modules with significant eigenmetabolite differences for TT and CQA, and possible compound interactions at this level. ConclusionsFour gene expression modules and two metabolite modules were altered by the four types of treatments applied. This methodology demonstrated the existence of both negative and positive interactions between TT, CQA and additional compounds found in CAW on the transcriptome and metabolome of mouse primary cortical neurons.

neuroscience↗

bioRxiv2024

Somatomotor disconnection links sleep duration with socioeconomic context, screen time, cognition, and psychopathology

BackgroundSleep is critical for healthy brain development and emotional wellbeing, especially during adolescence when sleep, behavior, and neurobiology are rapidly evolving. Theoretical reviews and empirical research have historically focused on how sleep influences mental health through its impact on higher-order brain systems. No studies have leveraged data-driven network neuroscience methods to uncover interpretable, brain-wide signatures of sleep duration in adolescence, their socio-environmental origins, or their consequences for cognition and mental health. MethodsHere, we implement graph theory and component-based predictive modeling to examine how a multimodal index of sleep duration is associated with intrinsic brain architecture in 3,173 youth (11-12 years) from the Adolescent Brain Cognitive DevelopmentSM Study. ResultsWe demonstrate that network integration/segregation exhibit a strong, generalizable multivariate association with sleep duration. We next identify a single component of brain architecture centered on a single network as the dominant contributor of this relationship. This component is characterized by increasing disconnection of a lower-order system - the somatomotor network - from other systems, with shorter sleep duration. Finally, greater somatomotor disconnection is associated with lower socioeconomic resources, longer screen times, reduced cognitive/academic performance, and elevated externalizing problems. ConclusionsThese findings reveal a novel neural signature of shorter sleep in adolescence that is intertwined with environmental risk, cognition, and psychopathology. By robustly elucidating the key involvement of an understudied brain system in sleep, cognition, and psychopathology, this study can inform theoretical and translational research directions on sleep to promote neurobehavioral development and mental health during the adolescent transition.

neuroscience↗

bioRxiv2024

The diversification of mealybugs was triggered by new symbiont acquisitions and followed by adaptive radiations on host plants

Symbiotic microorganisms play a critical role in supplementing beneficial nutrients to herbivorous insects feeding on unbalanced diets. These microbial symbionts can both facilitate or constrain plant-feeding insects adaptations to certain host plants, depending on their gene content and metabolic potential. The diet breadth of herbivorous insects is considered an important evolutionary factor affecting genotypic and phenotypic changes associated with host shifts. Acquiring new symbionts can, therefore, drive changes in niche breadth and subsequent adaptive radiation(s). Mealybugs comprise one of the major groups of scale insects, most of which feed on diverse angiosperms. Different sub-lineages of mealybugs also house different lineages of bacteria and fungi as their obligate symbionts. Here, we use mealybugs as a model system to test the hypothesis that the evolution of herbivorous insects is driven by both obligate symbionts and host plants. Based on metagenome analyses of 28 host species as well as a literature survey, we identified Betaproteobacteria, Gammaproteobacteria, Flavobacteriia, and Ophiocordyceps fungi as obligate symbionts of the major clades of mealybugs. A time-calibrated phylogenetic tree of mealybugs allowed us to infer the ancestral obligate symbionts of the major mealybug clades. Our results indicate that the emergence of major mealybug lineages coincided with the acquisitions of new obligate endosymbionts. Subsequent radiations of mealybugs were inferred to have mostly resulted from the adaptive radiation through continuous host shifts on angiosperms. The contribution of microbial symbiosis to the diversification of herbivorous insects is thus likely limited by new symbiont origins or replacements, and insect adaptations play a larger role in further plant switches.

evolutionary biology↗

bioRxiv2024

Accelerated pseudogenization in ancient endosymbionts of giant scale insects

Symbiotic microorganisms are subject to a complex interplay of environmental and population-genetic pressures that drive their gene loss. Despite the widely held perception that ancient symbionts have stable genomes, even tiny genomes experience ongoing pseudogenization. Whether these tiny genomes also experience bursts of rapid gene loss is, however, less understood. Giant scale insects (Monophlebidae) feed on plant sap and rely on the symbiotic bacterium Walczuchella which provides them with essential nutrients. When compared to other ancient symbionts with similar genome sizes such as Karelsulcia, Walczuchellas genome was previously reported as unusually pseudogene-rich (10 % of coding sequences). However, this result was based on only one genome assembly raising questions about the assembly quality or a recent ecological shift such as co-symbiont acquisition driving the gene loss. Here, we generated six complete genomes of Walczuchella from three genera of giant scales, each with distinct co-symbiotic partners. We show that all the genomes are highly degraded and particularly genes related to the cellular envelope and energy metabolism seem to be ongoing pseudogenization. Apart from general mechanisms driving genome reduction such as the long-term intracellular lifestyle with transmission bottlenecks, we hypothesize that a more profound loss of DNA replication and repair genes together with recent co-obligate symbiont acquisitions likely contribute to the accelerated degradation of Walczuchella genomes. Our results highlight that even ancient symbionts with small genomes can experience significant bursts of gene loss when stochastic processes erase a gene that accelerates gene loss or when the selection pressure changes such as after cosymbiont acquisition.

genomics↗

bioRxiv2024

Physics-inspired accuracy estimator for model-docked ligand complexes

Model docking, which refers to ligand docking into the protein model structures, is becoming a promising avenue in drug discovery with the advances in artificial intelligence (AI)-based protein structure prediction. However, a significant challenge remains; even when sampling was successful in model docking, typical docking score functions fail to identify correct solutions for two-thirds of them. This discrepancy between scoring and sampling majorly arises because these scoring functions poorly tolerate minor structural inaccuracies. In this work, we propose a deep neural network named DENOISer to address the scoring challenge in model-docking scenario. In the network, ligand poses are ranked by the consensus score of two independent sub-networks: the Native-likeness prediction and the Binding energy prediction networks. Both networks incorporate physical knowledge as inductive bias in order to enhance pose discrimination power while ensuring tolerance to small interfacial structural noises. Combined with Rosetta GALigandDock sampling, DENOISer outperformed existing docking tools on the PoseBusters model-docking benchmark set as well as on a broad cross-docking benchmark set. Further analyses reveal the physics-based components and the consensus ranking approach are the two most crucial factors contributing to its ranking success. We expect DENOISer may assist future drug discovery endeavors by providing more accurate structural models for protein-ligand complexes. The network is freely available at https://github.com/HParklab/DENOISer.

bioinformatics↗

bioRxiv2024

Microglia regulate GABAergic neurogenesis in prenatal human brain through IGF1

GABAergic neurons are an essential cellular component of neural circuits. Their abundance and diversity have enlarged significantly in the human brain, contributing to the expanded cognitive capacity of humans. However, the developmental mechanism of the extended production of GABAergic neurons in the human brain remains elusive. Here, we use single-cell transcriptomics, bioinformatics, and histological analyses to uncover microglial regulation of the sustained proliferation of GABAergic progenitors and neuroblasts in the human medial ganglionic eminence (hMGE). We show that insulin-like growth factor 1 (IGF1) and its receptor IGR1R as the top ligand-receptor pair underlying microglia-progenitor communication in the prenatal human brain. Using our newly developed neuroimmune hMGE organoids, which mimics hMGE cytoarchitecture and developmental trajectory, we demonstrate that microglia-derived IGF1 promotes progenitor proliferation and the production of GABAergic neurons. Conversely, IGF1-neutralizing antibodies and IGF1 knockout human embryonic stem cells (hESC)-induced microglia (iMG) completely abolished iMG-mediated progenitor proliferation. Together, these findings reveal a previously unappreciated role of microglia-derived IGF1 in promoting proliferation of neural progenitors and the development of GABAergic neurons.

neuroscience↗

bioRxiv2024

Sciphy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data.

CRISPR-based lineage tracing offers a promising avenue to decipher single cell lineage trees, especially in organisms that are challenging for microscopy. A recent advancement in this domain is lineage tracing based on sequential genome editing, which not only records genetic edits but also the order in which they occur. To capitalize on this enriched data, we introduce SciPhy, a simulation and inference tool integrated within the BEAST 2 framework. SciPhy utilizes a Bayesian phylogenetic approach to estimate time-scaled phylogenies and cell population parameters. After validating SciPhy using simulations, we apply it to two lineage tracing datasets for which we estimate time-scaled trees together with cell proliferation rates. Using simulated and real lineage tracing data obtained from a monoclonal culture of HEK293T cells, we compare SciPhy to other lineage reconstruction methods, and find that SciPhy consistently constructs distinct, and more accurate lineage trees. In particular, for HEK293T cells, SciPhy trees stand out for their later estimated cell division times. In addition, SciPhy reports uncertainty as well as proliferation rates, neither of which are available within a UPGMA analysis. Our second example applies SciPhy to the study of murine gastruloids, and showcases the use of complex models of time-varying population growth to capture realistic aspects of this in-vitro model of early mammalian development. Together, these examples showcase the application of advanced phylogenetic and phylodynamic tools to explore and quantify cell lineage trees, laying the groundwork for enhanced and confident analyses to decode the complexities of biological development in multicellular organisms. SciPhys codebase is publicly available at https://github.com/azwaans/SciPhy.

developmental biology↗