The developing human brain displays unique features that are difficult to study in animal models. Current in vitro models based on human brain tissue face several challenges, including the limited cellular heterogeneity in two- or three-dimensional neural stem cell cultures, while tissue slice cultures suffer from short survival. We recently established culture conditions to derive organoid cultures directly from human fetal brain tissue by preserving tissue integrity, which can be long-term expanded and display cellular heterogeneity and complex organization. In this Protocol, we describe detailed procedures to establish human fetal brain organoids (FeBOs) that broadly retain regional characteristics, along with procedures for their passaging and characterization. In addition, we describe genome engineering approaches applied to FeBOs to generate mutant FeBO lines that serve as versatile bottom-up brain cancer models. Lastly, we exemplify various downstream applications applicable to both healthy and mutant FeBOs. Scientists with experience in tissue culture can expect the establishment of human FeBO cultures to take 2-3 weeks, while genome engineering of FeBOs takes 2-4 months.

Cell-matrix interactions, mediated by cellular force and matrix remodeling, result in dynamic reciprocity that drives numerous biological processes and disease progression. Currently, there is no available method for directly quantifying cell traction force and matrix remodeling in three-dimensional matrices as a function of time. To address this long-standing need, we developed a high-resolution microfabricated device that enables longitudinal measurement of cell force, matrix stiffness and the application of mechanical stimulation (tension or compression) to cells. Here a specimen comprising of cells and matrix self-assembles and self-integrates with the sensor. With primary fibroblasts, cancer cells and neurons we have demonstrated the feasibility of the sensor by measuring single or multiple cell force with a resolution of 1 nN and changes in tissue stiffness due to matrix remodeling by the cells. The sensor can also potentially be translated into a high-throughput system for clinical assays such as patient-specific drug and phenotypic screening. We present the detailed protocol for manufacturing the sensors, preparing experimental setup, developing assays with different tissues and for imaging and analyzing the data. Apart from microfabrication of the molds in a cleanroom (one time operation), this protocol does not require any specialized skillset and can be completed within 4-5 h.

Glycosylated RNAs (glycoRNAs) have recently emerged as a new class of molecules of substantial interest owing to their potential roles in cellular processes and diseases. However, studying glycoRNAs is challenging owing to the lack of effective research tools including, but not limited to, imaging techniques to study the spatial distribution of glycoRNAs. Recently, we reported the development of a glycoRNA imaging technique, called sialic acid aptamer and RNA in situ hybridization-mediated proximity ligation assay (ARPLA), to visualize sialic acid-containing glycoRNAs with high sensitivity and specificity. Here we describe the experimental design principles and detailed step-by-step procedures for ARPLA-assisted glycoRNA imaging across multiple cell types. The procedure includes details for target selection, oligo design and preparation, optimized steps for RNA in situ hybridization, glycan recognition, proximity ligation, rolling circle amplification and a guideline for image acquisition and analysis. With properly designed probe sets and cells prepared, ARPLA-based glycoRNA imaging can typically be completed within 1 d by users with expertise in biochemistry and fluorescence microscopy. The ARPLA approach enables researchers to explore the spatial distribution, trafficking and functional contributions of glycoRNAs in various cellular processes.

Antibody-based research applications are critical for biological discovery. Yet there are no industry standards for comparing the performance of antibodies in various applications. We describe a knockout cell line-based antibody characterization platform, developed and approved jointly by industry and academic researchers, that enables the systematic comparison of antibody performance in western blot, immunoprecipitation and immunofluorescence. The scalable protocols, which require minimal technological resources, consist of (1) the identification of appropriate cell lines for antibody characterization studies, (2) development/contribution of isogenic knockout controls, and (3) a series of antibody characterization procedures focused on the most common applications of antibodies in research. We provide examples of expected outcomes to guide antibody users in evaluating antibody performance. Central to our approach is advocating for transparent and open data sharing, enabling a community effort to identify specific antibodies for all human proteins. Mid-level graduate students with training in biochemistry and prior experience in cell culture and microscopy can complete the protocols for a specific protein within 1 month while working part-time on this effort. Antibody characterization is needed to meet standards for resource validation and data reproducibility, which are increasingly required by journals and funding agencies.

RNAs play critical roles in most biological processes. Although the three-dimensional (3D) structures of RNAs primarily determine their functions, it remains challenging to experimentally determine these 3D structures due to their conformational heterogeneity and intrinsic dynamics. Cryogenic electron microscopy (cryo-EM) has recently played an emerging role in resolving dynamic conformational changes and understanding structure-function relationships of RNAs including ribozymes, riboswitches and bacterial and viral noncoding RNAs. A variety of methods and pipelines have been developed to facilitate cryo-EM structure determination of challenging RNA targets with small molecular weights at subnanometer to near-atomic resolutions. While a wide range of conditions have been used to prepare RNAs for cryo-EM analysis, correlations between the variables in these conditions and cryo-EM visualizations and reconstructions remain underexplored, which continue to hinder optimizations of RNA samples for high-resolution cryo-EM structure determination. Here we present a protocol that describes rigorous screenings and iterative optimizations of RNA preparation conditions that facilitate cryo-EM structure determination, supplemented by cryo-EM data processing pipelines that resolve RNA dynamics and conformational changes and RNA modeling algorithms that generate atomic coordinates based on moderate- to high-resolution cryo-EM density maps. The current protocol is designed for users with basic skills and experience in RNA biochemistry, cryo-EM and RNA modeling. The expected time to carry out this protocol may range from 3 days to more than 3 weeks, depending on the many variables described in the protocol. For particularly challenging RNA targets, this protocol could also serve as a starting point for further optimizations.

Skeletal myofibers naturally regenerate after damage; however, impaired muscle function can result in cases when a prominent portion of skeletal muscle mass is lost, for example, following traumatic muscle injury. Volumetric muscle loss can be modeled in mice using a surgical model of muscle ablation to study the pathology of volumetric muscle loss and to test experimental treatments, such as the implantation of acellular scaffolds, which promote de novo myogenesis and angiogenesis. Here we provide step-by-step instructions to perform full-thickness surgical ablation, using biopsy punches, and to remove a large volume of the tibialis anterior muscle of the lower limb in mice. This procedure results in a reduction in muscle mass and limited regeneration capacity; the approach is easy to reproduce and can also be applied to larger animal models. For therapeutic applications, we further explain how to implant bioscaffolds into the ablated muscle site. With adequate training and practice, the surgical procedure can be performed within 30 min.

High-throughput RNA sequencing enables the quantification of transcript abundance and the identification of novel transcripts in biological samples. These include circular RNAs (circRNAs), a family of alternatively spliced RNA molecules that form a continuous loop. However, quantification and comparison of circRNAs between RNA sequencing libraries remain challenging due to confounding errors introduced during exonuclease digestion, library preparation and RNA sequencing itself. Here we describe a set of synthetic circRNA spike-ins-termed 'SynCRS'-that can be added directly to purified RNA samples before exonuclease digestion and library preparation. SynCRS, introduced either individually or in combinations of varying size and abundance, can be integrated into all next-generation sequencing workflows and, critically, facilitate the quantitative calibration of circRNA transcript abundance between samples, tissue types, species and laboratories. Our step-by-step protocol details the generation of SynCRS and guides users on the stoichiometry of SynCRS spike-in to RNA samples, followed by the bioinformatic steps required to facilitate quantitative comparisons of circRNAs between libraries. The laboratory steps to produce the SynCRS require an additional 3 d on top of the high throughput circRNA sequencing and bioinformatics. The protocol is suitable for users with basic experience in molecular biology and bioinformatics.

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.