Cell Reports Methods最新文献

Despite the widespread popularity of the "scratch assay," where a pipette is dragged manually through cultured tissue to create a gap to study cell migration and healing, it carries significant drawbacks. Its heavy reliance on manual technique can complicate quantification, reduce throughput, and limit the versatility and reproducibility. We present an open-source, low-cost, accessible, robotic scratching platform that addresses all of the core issues. Compatible with nearly all standard cell culture dishes and usable directly in a sterile culture hood without specialized training, our robot makes highly reproducible scratches in a variety of complex cultured tissues with high throughput. Moreover, the robot demonstrates precise removal of tissues for sculpting arbitrary tissue and wound shapes, enabling complex co-culture experiments. This system significantly improves the usefulness of the conventional scratch assay and opens up new possibilities in complex tissue engineering for realistic wound healing and migration research.

Spatial transcriptomics is a groundbreaking technology, enabling simultaneous profiling of gene expression and spatial orientation within biological tissues. Yet when analyzing spatial transcriptomics data, effective integration of expression and spatial information poses considerable analytical challenges. Although many methods have been developed to address this issue, many are platform specific and lack the general applicability to analyze diverse datasets. In this article, we propose a method called the weighted ensemble method for spatial transcriptomics (WEST) that utilizes ensemble techniques to improve the performance and robustness of spatial transcriptomics data analytics. We compare the performance of WEST with six methods on both synthetic and real-world datasets. WEST represents a significant advance in detecting spatial domains, offering improved accuracy and flexibility compared to existing methods, making it a valuable tool for spatial transcriptomics data analytics.

Progress has been made in generating spinal cord and trunk derivatives from neuromesodermal progenitors (NMPs). However, maintaining the self-renewal of NMPs in vitro remains a challenge. In this study, we developed a cocktail of small molecules and growth factors that induces human embryonic stem cells to produce self-renewing NMPs (srNMPs) under chemically defined conditions. These srNMPs maintain the state of neuromesodermal progenitors in prolonged culture and have the potential to generate mesodermal cells and neurons, even at the single-cell level. Additionally, suspended srNMP aggregates can spontaneously differentiate into all tissue types of early embryonic trunks. Furthermore, transplanted srNMP-derived muscle satellite cells or progenitors of motor neurons were integrated into skeletal muscle or the spinal cord, respectively, and contributed to regeneration in mouse models. In summary, srNMPs hold great promise for applications in developmental biology and as renewable cell sources for cell therapy for trunk and spinal cord injuries.

Conventional static culture of organoids necessitates weekly manual passaging and results in nonhomogeneous exposure of organoids to nutrients, oxygen, and toxic metabolites. Here, we developed a miniaturized spinning bioreactor, RPMotion, specifically optimized for accelerated and cost-effective culture of epithelial organoids under homogeneous conditions. We established tissue-specific RPMotion settings and standard operating protocols for the expansion of human epithelial organoids derived from the liver, intestine, and pancreas. All organoid types proliferated faster in the bioreactor (5.2-fold, 3-fold, and 4-fold, respectively) compared to static culture while keeping their organ-specific phenotypes. We confirmed that the bioreactor is suitable for organoid establishment directly from biopsies and for long-term expansion of liver organoids. Furthermore, we showed that after accelerated expansion, liver organoids can be differentiated into hepatocyte-like cells in the RPMotion bioreactor. In conclusion, this miniaturized bioreactor enables work-, time-, and cost-efficient organoid culture, holding great promise for organoid-based fundamental and translational research and development.

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches and better sensitivity over popular differential abundance testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.

There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype. Existing subtyping approaches struggle to handle diverse data types with missing information. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can accommodate any feature that can be compared with a similarity metric to create subtypes versatilely. It can combine arbitrary data types for subtype discovery, such as merging genetic, transcriptomic, proteomic, and pathway data. iSubGen recapitulates known subtypes across multiple cancers even with substantial missing data and identifies subtypes with distinct clinical behaviors. It performs equally with or superior to other subtyping methods, offering greater stability and robustness to missing data and flexibility to new data types. It is available at https://cran.r-project.org/web/packages/iSubGen.

Protein natural diversity offers a vast sequence space for protein engineering, and deep learning enables its detection from metagenomes/proteomes without prior assumptions. DeepMetagenome, a Python-based method, explores protein diversity through modules for training and analyzing sequence datasets. The deep learning model includes Embedding, Conv1D, LSTM, and Dense layers, with sequence feature analysis for data cleaning. Applied to metallothioneins from a database of over 146 million coding features, DeepMetagenome identified over 500 high-confidence metallothionein sequences, outperforming DIAMOND and CNN-based models. It showed stable performance compared to a Transformer-based model over 25 epochs. Among 23 synthesized sequences, 20 exhibited metal resistance. The tool also successfully explored the diversity of three additional protein families and is freely available on GitHub with detailed instructions.

Genetically encoded inhibitors of Ca_V1 channels that operate via C-terminus-mediated inhibition (CMI) have been actively pursued. Here, we advance the design of CMI peptides by proposing a membrane-anchoring tag that is sufficient to link the inhibitory modules to the target channel as well as chemical and optogenetic modes of system control. We designed and implemented the constitutive and inducible CMI modules with appropriate dynamic ranges for the short and long variants of Ca_V1.3, both naturally occurring in neurons. Upon optical (near-infrared-responsive nanoparticles) and/or chemical (rapamycin) induction of FRB/FKBP binding, the designed peptides translocated onto the membrane via FRB-Ras, where the physical linkage requirement for CMI could be satisfied. The peptides robustly produced acute, potent, and specific inhibitions on both recombinant and neuronal Ca_V1 activities, including Ca²⁺ influx-neuritogenesis coupling. Validated through opto-chemogenetic induction, this prototype demonstrates Ca²⁺ channel modulation via membrane-assisted molecular linkage, promising broad applicability to diverse membrane proteins.

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.

In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening off-target effects. We hypothesized that the disease specificity of targets can be systematically learned for all genes by jointly evaluating complementary molecular measurements of healthy tissues using a hierarchical Bayesian modeling approach. Our method, BayesTS, integrates protein and gene expression evidence and includes tunable parameters to moderate tissue essentiality. Applied to all protein coding genes, BayesTS outperforms alternative strategies to define therapeutic targets and nominates previously unknown targets while allowing for incorporation of new types of modalities. To expand target repertoires, we show that extension of BayesTS to splicing antigens and combinatorial target pairs results in more specific targets for therapy. We expect that BayesTS will facilitate improved target prioritization for oncology drug development, ultimately leading to the discovery of more effective and safer treatments.