Cell Reports Methods最新文献

The tumor microenvironment (TME) comprises all non-tumor elements of cancer and strongly influences disease progression and phenotype. To understand tumor biology and accurately test new therapeutic strategies, representative models should contain both tumor cells and normal cells of the TME. Here, we describe and characterize co-culture tumor-derived organoids and cancer-associated fibroblasts (CAFs), a major component of the TME, in matrix-embedded assembloid models of esophageal adenocarcinoma (EAC). We demonstrate that the assembloid models faithfully recapitulate the differentiation status of EAC and different CAF phenotypes found in the EAC patient TME. We evaluate cell phenotypes by combining tissue-clearing techniques with whole-mount immunofluorescence and histology, providing a practical framework for the characterization of cancer assembloids.

Human intestinal organoids (HIOs) derived from human pluripotent stem cells (hPSCs) are promising resources for intestinal regenerative therapy as they recapitulate both endodermal and mesodermal components of the intestine. However, due to their hPSC-line-dependent mesenchymal development and spherical morphology, HIOs have limited applicability beyond basic research and development. Here, we demonstrate the incorporation of separately differentiated mesodermal and mid/hindgut cells into assembled spheroids to stabilize mesenchymal growth in HIOs. In parallel, we generate tubular intestinal constructs (assembled human intestinal tubules [a-HITs]) by leveraging the high aggregative property of assembled spheroids. Through rotational culture in a bioreactor, a-HITs self-organize to develop epithelium and supportive mesenchyme. Upon mesenteric transplantation, a-HITs mature into centimeter-scale tubular intestinal tissue with complex architectures. Our aggregation- and suspension-based approach offers basic technology for engineering tubular intestinal tissue from hPSCs, which could be ultimately applied to the generation of the human intestine for clinical application.

Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, enhancing our ability to identify therapeutic targets for disease interventions.

Detecting strain-specific barcodes with mass spectrometry can facilitate the screening of genetically engineered bacterial libraries. Here, we introduce intact protein barcoding, a method to measure protein-based library barcodes and metabolites using flow injection mass spectrometry (FI-MS). Protein barcodes are based on ubiquitin with N-terminal tags of six amino acids. We demonstrate that FI-MS detects intact ubiquitin proteins and identifies the mass of N-terminal barcodes. In the same analysis, we measured relative concentrations of primary metabolites. We constructed six ubiquitin-barcoded CRISPR interference (CRISPRi) strains targeting metabolic enzymes and analyzed their metabolic profiles and ubiquitin barcodes. FI-MS detected barcodes and distinct metabolome changes in CRISPRi-targeted pathways. We demonstrate the scalability of intact protein barcoding by measuring 132 ubiquitin barcodes in microtiter plates. These results show that intact protein barcoding enables fast and simultaneous detection of library barcodes and intracellular metabolites, opening up new possibilities for mass spectrometry-based barcoding.

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.

Multi-locus sequence typing (MLST) based on eight genes has become the method of choice for Borrelia typing and is extensively used for population studies. Whole-genome sequencing enables studies to scale up to genomic levels but necessitates extended schemes. We have developed a 639-loci core genome MLST (cgMLST) scheme for Borrelia burgdorferi sensu lato (s.l.) that enables unambiguous genotyping and improves the robustness of phylogenies and lineage resolution within species. Notably, all inner nodes of the cgMLST phylogenies had consistently high statistical support. Analyses of the genetically homogeneous European B. bavariensis population support the notion that cgMLST provides high discriminatory power even for closely related isolates. While isolates differed maximally in one MLST locus, there were up to 179 cgMLST loci differences. Thus, the developed cgMLST scheme for B. burgdorferi s.l. resolves lineages at a finer resolution than MLST and improves insights into the evolutionary history of the species complex.

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.

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.

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.

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.