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At least 217 records · Page 12Linked to original sources

bioRxiv2022

Construction of a High-Density Genetic Map Based on Large-Scale Marker Development in Coix lacryma-jobi L . Using Specifific-Locus Amplifified Fragment Sequencing (SLAF-seq)

Coix lacryma-jobi L. is one of the most important economical and medicine corn. In this study, a high genetic linkage map has been constructed for Coix lacryma-jobi L. from a cross F2 community of "Qianyi NO.2" x "Wenyi NO.2" and their parents through high-throughput sequencing and specfic-locus amplified fragment (SLAF) library construction. After pre-processing, 325.49 Gb of raw data containing 1,628 M reads were obtained. A total of 22,944 high-quality SLAFs were detected, among which 3,952 SLAFs and 3,646 of the polymorphic markers could meet the requirements for construction of a genetic linkage map. The integrated map contains 3,605 high quality SLAFs, which are grouped to 10 genetic linkage groups. The total length of the linkage map is 1,620.39 cM with an average distance of 0.45 cM and 360.5 markers in average of per linkage group. This work will provide an important molecular biology basis for investigating their characteristics, gene cloning, molecular marker-assisted breeding, functional genomics and so on for Jobs tears.

genetics↗

bioRxiv2024

Scalable Guide Tree Construction Using Quantum Annealing for Multiple Sequence Alignment

Multiple sequence alignment (MSA) reveals homology in biological sequences, which is crucial for phylogenetics, medicine, and molecular biology. Many heuristic MSA algorithms use guide trees to determine sequence alignment order, but finding an optimal guide tree is an NP-hard problem. Conventional guide tree algorithms are greedy heuristics designed for better scalability at the cost of accuracy. By utilizing quantum algorithms, such as quantum annealing, we can overcome the problem of local minima that occurs in greedy methods. We propose a scalable guide tree algorithm that achieves scalability through quantum annealing. The theoretical foundations of our method are both minimum evolution and molecular clock. Unlike classical greedy approaches, we directly map the minimum evolution problem to a traveling salesperson problem (TSP), which quantum annealing can solve efficiently. The TSP solution enables the guide tree to be constructed in linear time using the molecular clock. Even with only a single sample from a D-Wave hybrid solver, our guide tree generally performed comparably to classical trees on the BAliBASE 3.0 benchmark. With a single iterative refinement, no statistically significant performance differences were found between our method and classical guide trees. Our scalable guide tree algorithm is practical in the sense that only a single sampling can be enough for constructing a good guide tree. Further performance analysis requires larger-scale benchmark tests. Fortunately, rapid advances in quantum hardware may soon enable these tests. While the practical application of quantum algorithms in bioinformatics has been relatively overlooked, this study highlights the potential of quantum algorithms for targeting computational bottlenecks in the field.

bioinformatics↗

bioRxiv2025

Digital Phage Biology in Droplets

Since their discovery, bacteriophages - viruses that infect bacteria - have been an invaluable source of insight for molecular biology and have provided a plethora of molecular components for biotechnology. Driven by the imminent global antibiotic crisis, interest in using bacteriophages as antimicrobial agents has strongly increased in the past few years. While standard quantification of phage counts and infectivity by double-layer plaque assays (DLA) have provided foundational insights, they are limited by their inability to monitor infection dynamics over time and the inflexibility in experimental setups. Here, we introduce a novel high-throughput method using droplet microfluidics to quantify individual phage infection events, enhancing the understanding of phage-host dynamics at the single-event level. By co-encapsulating individual phages and bacteria in microfluidic droplets, we can control key experimental parameters such as exposure time and the ratio of phages to bacteria. Our system allows for precise quantification of lysis events and the direct observation of lysis kinetics unaffected by the continuous release of progeny phages that is typical in bulk cultures. Moreover, our method is potentially applicable to any phage-host pair. Our findings suggest that droplet microfluidics can provide a more accurate and dynamic understanding of phage biology. This approach could be particularly valuable in the development of phage-based antimicrobial strategies in the face of rising antibiotic resistance.

biophysics↗

bioRxiv2023

Redox-enabled Electronic Interrogation and Feedback Control of Hierarchical and Networked Biological Systems

We enable microelectronic devices to interrogate biologys molecular communication, perform computations, and in real time control biological systems, including at several hierarchical levels: proteins, cells, and cell consortia. A key driver is establishing electronic access to and from biologys native redox networks. First, redox-mediated electro-biofabrication facilitates facile assembly of biological components onto microelectronic systems, then electrode-actuated redox allows digital programming of enzyme activity, and further, redox-mediated electrogenetics facilitates closed-loop electronic control of cellular function. Specifically, we show algorithm-based feedback control of enzyme activity, cellular genetic circuits (via eCRISPR) and cell consortia behavior, all enabled by electronic data transfer. We further demonstrate electronic switching of cell-cell quorum sensing communication from one autoinducer network to another, creating an electronically controlled "bilingual" cell. We suggest these methodologies will not only help us to better understand biological systems, but design and control those currently unimagined.

synthetic biology↗

bioRxiv2024

Computational Transcriptomic and Comparative Genomic Analysis of dead box RNA Helicase gene AT2G45810 Expressed in Plants Arabidopsis thaliana

Arabidopsis thaliana is a short life cycle, small genome, and Brassicaceae family winter annual small flowering plants. It is popularly used as a model organism in genetics and plant biology research, and it is essential to understanding the molecular biology of many plant features, including light sensing and flower formation. It also plays a key role in the science of agronomy, and plant transcriptomics as well as genomics. When it comes to the development of multicellular creatures, transcriptional programs are crucial. The constantly active growth of different organ systems is supported by transcriptional programs. Arabidopsis embryos possess remarkable transcriptomes compared to other plant tissues comprising somatic embryo differentiation circumstances operating during plant embryogenesis. Here we show that the transcriptomic analysis of the genome dead box RNA helicase gene AT2G45810 of Arabidopsis thaliana Araport11 species which revealed the specific gene expression patterns of Arabidopsis tissue-specific information of developmental map, embryo, single cell, DNA damage, cellular interactions, pathway analysis, etc., through In Silico or computational approaches. In this particular study, we used the TAIR, Phytozome, and plant comparative genomics portal for retrieving and identification of specific genes of interest. Next, we used web-based Bar utoronto tools to visualize other data, including functional genomics. Their protein and gene expression tools facilitate the exploration of promoters, the identification of protein-protein interactions, the viewing of expression patterns as electronic fluorescent pictographs or heatmaps, and more.

developmental biology↗

bioRxiv2025

Magneto-Photonic Gene Circuit for Minimally Invasive Control of Gene Expression in Mammalian Cells

Precise control of gene expression is one of the fundamental goals of synthetic biology. Whether the objective is to modify endogenous cellular function or induce the expression of molecules for diagnostic and therapeutic purposes, gene regulation remains a key aspect of biological systems. Over time, advances in protein engineering and molecular biology have led to the creation of gene circuits capable of inducing the expression of specific proteins in response to external stimulus such as light. These optogenetic, or light-activated circuits hold significant potential for gene therapy as a tool for regulating the expression of therapeutic genes within cells. However, the applications of optogenetic systems can be limited by the lack of efficient ways for light delivery inside cells or tissue. Our approach to address this challenge is to harness the power of bioluminescence to produce light directly inside cells using a luminescent enzyme. Combined with a photosensitive transcription factor, we report the development of a fully genetically encoded optogenetic circuit for control of gene expression. Furthermore, we utilized a magneto sensitive protein to engineer a split protein version of this luminescent enzyme, where its reconstitution is driven by a 50mT magnetic stimulus. Thus, resulting in a first-of-its-kind gene circuit activated by a combination of light and magnetic stimulus. We expect this work to advance the implementation of light-controlled systems without the need of external light sources, as well as serve as a basis for the development of future magneto-sensitive tools.

synthetic biology↗

bioRxiv2018

Synthetic Biology: Mapping the Patent Landscape

This article presents the global patent landscape for synthetic biology as a new and emerging area of science and technology. The aim of the article is to provide an overview of the emergence of synthetic biology in the patent system and to contribute to future research by providing a high quality tagged core dataset with 7,424 first filings and 71,887 family members. This dataset is intended to assist with evidence based exploration of synthetic biology in the patent system and with advancing methods for the analysis of new and emerging areas of science and technology.\n\nThe starting point for the research is recognition that traditional methods of patent landscape analysis based on key word searches face limitations when addressing new and emerging areas of science and technology. Synthetic biology can be broadly described as involving the design, synthesis and assembly of biological parts, circuits, pathways, cells and genomes. As such synthetic biology can be understood as emerging from a combination of overlaps and convergences between existing fields and disciplines, such as biotechnology, genetic engineering, protein engineering and systems biology. More precisely, synthetic biology can be understood as the synthetic phase of molecular biology and genetic engineering. This presents the problem that key word strategies may radically overestimate activity because they involve terms that are widely used in underlying fields that are contributing to synthetic biology.\n\nIn response to this problem we combined anthropology, scientometrics and data science to map authors from scientific publications on synthetic biology into the international patent system. We mapped 10,816 authors into the international patent system and identified 2,450 authors of articles on synthetic biology who are also inventors in the period to December 2017. By combining this data with citation information and a baseline keyword strategy we are able to describe the global patent landscape for synthetic biology and the wider patent universe in which synthetic biology is situated.\n\nThis article describes the main features of the global landscape and provides the tagged dataset as a contribution to evidence based debate on intellectual property and synthetic biology and methodological development. We anticipate that the data will prove useful in informing international policy debates on synthetic biology under the United Nations Convention on Biological Diversity.

synthetic biology↗

bioRxiv2025

ATAD3 megadalton complex in Plasmodium falciparum is essential for mitochondrial and cellular viability

Malaria remains an urgent threat to global health as the mortality and infection rates keep rising annually and our frontline antimalarials are becoming less effective due to the emergence and spread of resistance-conferring mutations. Although the mitochondrion of P. falciparum parasites is a validated drug target, there remain many uncharacterized mitochondrial proteins. The goal of this study was to investigate the essentiality and functions of a recently identified mitochondrial protein - PF3D7_0707400. Our results show that PF3D7_0707400 is an ATAD3A ortholog that is essential to parasite survival and is present in a megadalton complex that is critical for multiple mitochondrial processes such as mitochondrial RNA stability, membrane potential, ultrastructure, and protein import. This study is the first characterization, to our knowledge, of ATAD3A in unicellular organisms. ATAD3A has been previously studied in multicellular eukaryotes and has been implicated in several childhood mitochondrial diseases. Our findings here expand our knowledge on apicomplexan mitochondrial biology and our arsenal of potential antimalarial drug targets. Author SummaryEach year, malaria is responsible for about 200 million infections and 600,000 deaths across the world. Thus, it constitutes a huge global health crisis. Increasing rates of antimalarial resistance necessitates the identification and characterization of novel parasitic proteins that can be exploited for the development of new antimalarial therapeutics. To this end, the mitochondrion of P. falciparum parasites has been studied as a validated target for effective antimalarials. However, much remains to be understood about critical mitochondrial processes and proteins that are essential for mitochondrial viability and parasite survival. Our study details the first characterization of an ATAD3 protein in a unicellular eukaryote, specifically in an apicomplexan parasite. The conservation of this protein in these deep-branching organisms highlights the importance of its biological functions, further emphasizing the significance of our study. By employing advanced molecular biology techniques, we show the presence of PfATAD3 in a giant molecular complex and its essentiality in asexual P. falciparum parasites. Conditional knockdown of PfATAD3 resulted in defects in critical mitochondrial processes such as mitochondrial RNA stability, mitochondrial membrane potential, and mitochondrial morphology. Divergence of PfATAD3 from the host allows for exploitation of this protein as a target for new antimalarials.

microbiology↗

bioRxiv2018

The expression tractability of a biological trait

Understanding how gene expression is translated to phenotype is central to modern molecular biology, but the success is contingent on the intrinsic tractability of the specific traits under examination. However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable. Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate (ST) by gauging the number (N) of different expression states that underlie the same trait abnormality, with large ST corresponding to large N. By analyzing over 200 yeast morphological traits we show that ST is constrained by natural selection, which builds co-regulated gene modules to minimize the total number of possible expression states. We further show that ST is a good measure of the titer of recurrent patterns of an expression-trait relationship, predicting the extent to which the trait could be deterministically understood with gene expression data.

genomics↗

bioRxiv2024

CPT1A Mediates Radiation Sensitivity in Colorectal Cancer

The prevalence and mortality rates of colorectal cancer (CRC) are increasing worldwide. Radiation resistance hinders radiotherapy, a standard treatment for advanced CRC, leading to local recurrence and metastasis. Elucidating the molecular mechanisms underlying radioresistance in CRC is critical to enhance therapeutic efficacy and patient outcomes. Bioinformatic analysis and tumour tissue examination were conducted to investigate the CPT1A mRNA and protein levels in CRC and their correlation with radiotherapy efficacy. Furthermore, lentiviral overexpression and CRISPR/Cas9 lentiviral vectors, along with in vitro and in vivo radiation experiments, were used to explore the effect of CPT1A on radiosensitivity. Additionally, transcriptomic sequencing, molecular biology experiments, and bioinformatic analyses were employed to elucidate the molecular mechanisms by which CPT1A regulates radiosensitivity. CPT1A was significantly downregulated in CRC and negatively correlated with responsiveness to neoadjuvant radiotherapy. Functional studies suggested that CPT1A mediates radiosensitivity, influencing reactive oxygen species (ROS) scavenging and DNA damage response. Transcriptomic and molecular analyses highlighted the involvement of the peroxisomal pathway. Mechanistic exploration revealed that CPT1A downregulates the FOXM1-SOD1/SOD2/CAT axis, moderating cellular ROS levels after irradiation and enhancing radiosensitivity. CPT1A downregulation contributes to radioresistance in CRC by augmenting the FOXM1-mediated antioxidant response. Thus, CPT1A is a potential biomarker of radiosensitivity and a novel target for overcoming radioresistance, offering a future direction to enhance CRC radiotherapy.

cancer biology↗

bioRxiv2025

Tomato Omics Research: Mapping Bibliometric Footprints of Global Research Trends, Collaboration Networks, and Impact Trajectories

Tomato is a globally vital crop and model system for fleshy fruit development, with profound agricultural and economic significance. Omics-based tomato research has revolutionized understanding of fruit quality traits, disease resistance mechanisms, and stress tolerance pathways reflecting insights for breeding innovations to address food security challenges. This bibliometric analysis on tomato omics delivered crucial insights into the evolution and impact of tomato omics research over two decades. Examining 1,702 Scopus-indexed publications, we have shown a remarkable acceleration in research output after 2017, signifying the fields growing significance. Our findings revealed that Biochemistry, Genetics and Molecular Biology dominated the landscape with 1,054 publications, establishing the molecular foundation for tomato research. It identified key global research hubs, with the United States (450 publications) leading, followed by China (310) and Italy (171), while mapping eight distinct International collaboration networks that drive innovation. Through citation analysis, we have pinpointed transformative contributions in microRNA research and genome sequencing, with the fields most influential paper garnering 2,570 citations. Co-authorship network visualization exposed 25 research clusters among 8,103 authors, highlighting prolific contributors including Fernie A.R. (39 publications) and Lucini L. (26). The keyword co-occurrence mapping revealed critical research priorities centered on stress responses, fruit quality, and plant immunity, with "Frontiers in Plant Science" emerging as the fields premier publication venue (92 articles). Our analysis provided a roadmap of tomato omics research progression, offering strategic value for prioritization, collaboration development, and funding allocation to address pressing challenges in global food security and crop improvement.

plant biology↗

bioRxiv2018

Toward deciphering developmental patterning with deep neural network

Complex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation - the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN "black box". Our interpretable DNN approach should have broad applications in genotype-phenotype mapping. SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors.

developmental biology↗

bioRxiv2021

Directed evolution of colE1 plasmid replication compatibility: a fast tractable tunable model for investigating biological orthogonality.

Plasmids of the ColE1 family are among the most frequently used plasmids in molecular biology. They were adopted early in the field for many biotechnology applications, and as model systems to study plasmid biology. The mechanism of replication of ColE1 plasmids is well understood, involving the interaction between a plasmid-encoded sense-antisense gene pair (RNAI and RNAII). Because of its mechanism of replication, bacterial cells cannot maintain two different plasmids with the same origin, with one being rapidly lost from the population - a process known as plasmid incompatibility. While mutations in the regulatory genes RNAI and RNAII have been reported to make colE1 plasmids more compatible, there has been no attempt to engineer compatible colE1 origins, which can be used for multi-plasmid applications and that can bypass design constrains created by the current limited plasmid origin repertoire available. Here, we show that by targeting sequence diversity to the loop regions of RNAI (and RNAII), it is possible to select new viable colE1 origins that are compatible with the wild-type one. We demonstrate origin compatibility is not simply determined by sequence divergence in the loops, and that pairwise compatibility is not an accurate guide for higher order interactions. We identify potential principles to engineer plasmid copy number independently from other regulatory strategies and we propose plasmid compatibility as a tractable model to study biological orthogonality. New characterised plasmid origins increase flexibility and accessible complexity of design for challenging synthetic biology applications where biological circuits can be dispersed between multiple independent genetic elements.

synthetic biology↗

bioRxiv2021

Large-scale phylogenomics of the genus Macrostomum (Platyhelminthes) reveals cryptic diversity and novel sexual traits

Free-living flatworms of the genus Macrostomum are small and transparent animals, representing attractive study organisms for a broad range of topics in evolutionary, developmental, and molecular biology. The genus includes the model organism M. lignano for which extensive molecular resources are available, and recently there is a growing interest in extending work to additional species in the genus. These endeavours are currently hindered because, even though >200 Macrostomum species have been taxonomically described, molecular phylogenetic information and geographic sampling remain limited. We report on a global sampling campaign aimed at increasing taxon sampling and geographic representation of the genus. Specifically, we use extensive transcriptome and single-locus data to generate phylogenomic hypotheses including 145 species. Across different phylogenetic methods and alignments used, we identify several consistent clades, while their exact grouping is less clear, possibly due to a radiation early in Macrostomum evolution. Moreover, we uncover a large undescribed diversity, with 94 of the studied species likely being new to science, and we identify multiple novel morphological traits. Furthermore, we identify cryptic speciation in a taxonomically challenging assemblage of species, suggesting that the use of molecular markers is a prerequisite for future work, and we describe the distribution of possible synapomorphies and suggest taxonomic revisions based on our finding. Our large-scale phylogenomic dataset now provides a robust foundation for comparative analyses of morphological, behavioural and molecular evolution in this genus.

evolutionary biology↗

bioRxiv2019

DNA-BOT: A low-cost, automated DNA assembly platform for synthetic biology

Multi-part DNA assembly is the physical starting point for many projects in Synthetic and Molecular Biology. The ability to explore a genetic design space by building extensive libraries of DNA constructs is essential for creating programmed biological systems that perform the desired functions. With multiple DNA assembly methods and standards adopted in the Synthetic Biology community, automation of the DNA assembly process has received serious attention in recent years. Importantly, automating DNA assembly enables larger builds using less researcher time, increasing the accessible design space. However, these benefits currently incur high costs for both equipment and consumables. Here, we address this limitation by introducing low-cost DNA assembly with BASIC on OpenTrons (DNA-BOT). For this purpose, we developed an open-source software package dnabot (https://github.com/BASIC-DNA-ASSEMBLY/dnabot). We demonstrate the performance of DNA-BOT by simultaneously assembling 88 constructs composed of 10 genetic parts, exploring the promoter, ribosome binding site (RBS) and gene order design space for a 3-gene operon. All 88 constructs were assembled with high accuracy, at a cost of $1.50 - $5.50 per construct. This illustrates the efficiency, accuracy and affordability of DNA-BOT making it accessible for most labs and democratising automated DNA assembly.

synthetic biology↗

bioRxiv2024

Processivity of the monomeric KLP-6 kinesin and a Brownian ratchet model with symmetric potentials

Most kinesin molecular motors dimerize to move processively and efficiently along microtubules; however, some can maintain processivity even in a monomeric state. Previous studies have suggested that asymmetric potentials between the motor domain and microtubules underlie this motility. In this study, we demonstrate that the kinesin-3 family motor protein KLP-6 can move along microtubules as a monomer upon release of autoinhibition. This motility can be explained by a change in length between the head and tail, rather than by asymmetric potentials. Using mass photometry and single-molecule assays, we confirmed that activated full-length KLP-6 is monomeric both in solution and on microtubules. KLP-6 possesses a microtubule-binding tail domain, and its motor domain does not exhibit biased movement, indicating that the tail domain is crucial for the processive movement of monomeric KLP-6. We developed a mathematical model to explain the unidirectional movement of monomeric KLP-6. Our model concludes that a slight conformational change driven by neck-linker docking in the motor domain enables the monomeric kinesin to move unidirectionally if a second microtubule-binding domain exists. SIGNIFICANCEKinesin molecular motors are designed to move efficiently using two heads. Studying these biological molecular motors provides valuable insights into the mechanisms that generate unidirectional movements amidst intense thermal fluctuations. This study reveals that the monomeric kinesin-3 KLP-6 can move along microtubules through interactions with its tail domain. The proposed Brownian ratchet model explains this movement by considering a change in stalk length caused by neck-linker docking rather than asymmetric potentials. This model suggests that a slight conformational change can achieve robust processive movement of kinesin. These findings have significant implications for understanding Brownian ratchet motors and designing rational artificial molecular motors.

biophysics↗

bioRxiv2025

Near chromosome-level genome assembly of Neomusotima conspurcatalis gives insights into the evolution of moth genome architecture and fern-insect interactions

Plant-insect interactions are the foundation of ecosystems globally, yet we are still determining the underlying mechanisms through which these relationships evolve. The co-evolution between insects and their host plants should shape the genomes of both partners, and genes involved in interaction specificity should show unique genomic signatures (e.g., rapid evolution, gene family expansions). Biological control programs are an excellent system for disentangling the genomics and molecular biology of the establishment of an insect and its host plant specificity. Fern-insect relationships are among the most poorly understood, and ferns have long been thought to have few interactions with insects, although recent evidence suggests that these relationships are under-sampled and studied. Here, we present a near-chromosome genome assembly of the crambid moth Neomusotima conspurcatalis, a biological control agent employed in the management of the invasive vining fern Lygodium microphyllum. We use this novel genomic resource to explore the evolution of genome architecture across the Crambidae, revealing highly conserved genome structure across this family of moths. We also examine gene family evolution across the phylogeny and identify expansions in odorant receptor gene families that may be involved in the highly specific interaction of N. conspurcatalis with L. microphyllum. This work highlights the utility of genomics in biological control, and the utility of biological control in informing fundamental understanding of plant-insect interactions. SummaryPlant-insect interactions form the basis of ecological communities and can be highly specific through long lasting co-evolution. Classical biological control provides excellent opportunities to investigate how insect and host genomes are shaped by co-evolutionary processes. We sequenced the genome of Neomusotima conspurcatalis, a biological control moth used to manage the invasive fern Lygodium microphyllum. We analyzed how the structure of the genome has evolved over time relative to other crambid moths and how expansions in gene families involved in odorant reception may be involved in co-evolution with its host. This work provides insight into herbivore interactions in seed-free vascular plants.

evolutionary biology↗

bioRxiv2021

Tabula Microcebus: A transcriptomic cell atlas of mouse lemur, an emerging primate model organism

Mouse lemurs are the smallest, fastest reproducing, and among the most abundant primates, and an emerging model organism for primate biology, behavior, health and conservation. Although much has been learned about their physiology and their Madagascar ecology and phylogeny, little is known about their cellular and molecular biology. Here we used droplet- and plate-based single cell RNA-sequencing to profile 226,000 cells from 27 mouse lemur organs and tissues opportunistically procured from four donors clinically and histologically characterized. Using computational cell clustering, integration, and expert cell annotation, we defined and biologically organized over 750 mouse lemur molecular cell types and their full gene expression profiles. These include cognates of most classical human cell types, including stem and progenitor cells, and the developmental programs for spermatogenesis, hematopoiesis, and other adult tissues. We also described dozens of previously unidentified or sparsely characterized cell types and subtypes. We globally compared cell type expression profiles to define the molecular relationships of cell types across the body, and explored primate cell and gene expression evolution by comparing mouse lemur cell transcriptomes to those of human, mouse, and macaque. This revealed cell type specific patterns of primate specialization, as well as many cell types and genes for which lemur provides a better human model than mouse. The atlas provides a cellular and molecular foundation for studying this primate model organism, and establishes a general approach for other emerging model organisms.

genetics↗