Articles in press

Adenosine-to-inosine (A-to-I) RNA editing, catalyzed by ADAR1, occurs on double-stranded RNA (dsRNA) and plays a key role in innate immunity. Editing of endogenous dsRNAs prevents aberrant activation of dsRNA sensors and resulting inflammation. This function of ADAR1 is essential in many cancers, making ADAR1 a promising therapeutic target. Current approaches for measuring A-to-I editing in biochemical assays, which often rely on Sanger sequencing, are limited in throughput. Thus, there is a need to develop high-throughput A-to-I editing assays to enable efficient screening of potential ADAR1 inhibitors. We developed a new high-throughput A-to-I editing assay that uses barcoded reverse-transcription primers to enable pooling and sequencing of multiple assays performed in parallel. Starting from an existing Sanger-based method, we optimized aspects of the A-to-I editing reaction, cDNA synthesis, and downstream processing to facilitate high-throughput assays and substantially lower per-reaction costs. In brief, immediately following the A-to-I editing reaction, cDNA is synthesized using well-specific barcoded reverse-transcription primers. Subsequently, cDNA from all reactions is pooled and processed as one sample for next-generation sequencing, with the potential for further multiplexing by indexed sequencing adapters. Our approach enables parallel measurement of RNA editing under multiple conditions in a plate-based format while significantly reducing the time, labor, and costs required compared to traditional methods. This high-throughput assay provides a scalable platform for screening ADAR1 inhibitors and may accelerate the development of targeted therapies for cancer.

Drosophila melanogaster has emerged as a powerful model for investigating the genetic control of muscle development and muscle function in both normal and pathological conditions. To date, several Drosophila models of human neuromuscular disorders (NMDs) have been generated and characterized, allowing a better understanding of disease mechanisms, improving diagnosis, and testing of new therapeutic strategies. Conserved genes encoding large muscle proteins such as Titin or Ryanodine receptor (RyR) harbor hundreds of variant mutations, many of which remain variants of unknown significance (VUS). Here, we describe procedures for generating a CRISPR-engineered Drosophila model of a VUS and an integrated workflow for quantitative assessment of functional and structural muscle properties, providing an indication of VUS pathogenicity. The protocol combines larval locomotor assays, muscle morphometry, and structural muscle properties assessment. The workflow enables systematic evaluation of muscle structure and function and provides a versatile platform for studying genetic variants and their involvement in NMDs.

Three-dimensional segmentation of cell nuclei is a fundamental step in quantitative studies of morphogenesis and early embryonic development, enabling the analysis of cellular organization and behavior in complex biological tissues. However, accurate segmentation of densely packed nuclei in large volumetric microscopy datasets remains technically challenging, as touching or closely apposed nuclei are frequently merged by instance-based segmentation approaches. Several deep learning–based approaches have been developed to address this problem, including convolutional neural network architectures and shape-based segmentation methods. Although these techniques have improved segmentation performance, their adoption is often limited by complex software installation procedures, demanding dataset annotation requirements and computational expertise. Here, we present a user-oriented workflow for 3D segmentation of nuclei in mouse embryos designed to simplify and speed up image annotation, requiring little manual intervention. The protocol combines a dual-channel imaging strategy, in which a high-resolution channel optimized for nuclear boundary detection is used to generate ground-truth annotations, while a lower-resolution DAPI channel serves as the training input. Rather than Cellpose, which is difficult to train and slow in 3D, or StarDist, which is difficult to use without programming skills, we suggest training Biom3d in its default nnCore version. The workflow includes semi-automated generation of training datasets, model training and evaluation through a preconfigured Biom3d environment, and application of the trained model to new volumetric datasets. Automated preprocessing, prediction, and post-processing steps are integrated into the pipeline to facilitate high-throughput analysis. The trained model was evaluated with a Dice score of 0.81. This protocol provides an accessible and reproducible solution for non-specialist users by minimizing manual annotation efforts and eliminating most software configuration requirements. The combination of dual-channel training data generation and a preconfigured deep learning framework enables scalable and robust 3D nuclear segmentation with limited computational expertise.

Paired transcriptomic and proteomic profiling is a central approach in systems biology for understanding how gene-expression programs relate to protein abundance across biological conditions. In recent years, a variety of computational methods have been developed to analyze such data, including correlation-based analyses, regression models, partial least-squares approaches, and matrix-factorization techniques, while more recent deep-learning methods aim to capture nonlinear cross-omic relationships but typically require large numbers of paired samples. In these studies, the dimensionality of each assay may be large, yet the number of paired transcriptomic and proteomic observations (samples) is often small because every condition requires coordinated sample collection, sequencing, mass spectrometry, and downstream processing. This imbalance creates a challenging learning problem: a model must estimate cross-omic relationships from only a few paired samples. MintOmics addresses this limited-sample setting through masked transcriptome-to-proteome inference. The workflow pairs biological samples, filters and normalizes bulk RNA-seq and LC-MS/MS data, maps gene symbols to protein accessions, partitions biological units to prevent information leakage, and generates randomly masked computational views within each partition. A transformer is then trained to predict the abundance classes of proteins corresponding to randomly masked transcripts from the remaining unmasked transcriptomic context, followed by held-out inference, attention interpretation, and downstream enrichment analysis. Tested on the transcriptomics and proteomics data of the mouse oviduct, this approach enables the prediction of protein abundance states from transcriptomic profiles when paired multi-omics observations are limited. Its attention-based outputs provide an interpretable view of the transcriptomic features (e.g., key transcription factors) contributing to protein abundance predictions, thereby supporting downstream analyses of biological processes and regulatory mechanisms.

Obtaining a functionally active mitochondrial protease complex ClpXP using the Escherichia coli–based expression system is complicated by two factors: (i) complex hetero-oligomeric structure of this multi-subunit complex requires correct assembly of the subunits in heterological environment, while (ii) restoration of the catalytic activity during the expression is toxic for the host cells. This paper presents a simple and low-cost approach that combines separate expression of two ClpXP subunits in insoluble form, their combined purification from inclusion bodies by Ni-NTA metal affinity chromatography, and refolding of the active hetero-oligomeric protein directly on the column by stepwise decreasing the urea concentration. Separate expression allows individual optimization of expressional conditions. The insoluble state of expressed subunits prevents host toxicity and increases the yield. The combined denaturation and immobilization on the column promote the formation of a native hetero-oligomeric structure during refolding, enabling the isolation of the enzyme in its active form. Elution with imidazole ensures the release of correctly folded protein immediately after the removal of denaturing stress, minimizing the time the protease remains in a partially denatured state. The proposed protocol is reproducible and can be applied to the production of other hetero-oligomeric recombinant proteases in active forms.

CRISPR/Cas12a-based nucleic acid detection enables rapid molecular diagnostics, yet conventional two-step assays are slow and contamination-prone, while one-pot methods suffer from Cas12a cis-cleavage of amplicons. This protocol presents a one-pot, cis-cleavage-free SCas12a system using a bipartite split crRNA—comprising a scaffold and 20-nt spacer—that directs Cas12a to PAM-free double-stranded DNA with no detectable cis-cleavage under the tested conditions while maintaining trans-cleavage of ssDNA reporters. We detail RNA/primer design, ortholog expression and purification, RNP assembly, and integration with RPA, RT-RPA, or LAMP for unified detection and single-nucleotide variant (SNV) genotyping. The original study demonstrated improved sensitivity and single-base discrimination in selected model assays, with clinical application examples involving HPV16, SARS-CoV-2, and TP53. These results support potential point-of-care adaptation; broader clinical performance and portable readout remain to be validated.

Plant-parasitic cyst nematodes affect crop growth and cause substantial yield losses. Upon invading plant roots, cyst nematodes convert a few hundred cells from the vascular tissues into a feeding structure, called the syncytium. The nematodes use the syncytium to withdraw nutrients from the host plant, which serves as their sole source of nutrition. The molecular mechanisms underlying feeding site initiation and development remain poorly understood. One of the key factors involved in this process is transcriptional changes in host plants induced by nematode infection. To date, various transcriptomic techniques, including sc-RNAseq and RNA tomography, have been used to study host transcriptional changes during nematode infection. Although these studies provide valuable information, confirmation of the spatial gene expression of candidate genes during syncytium development remains limited. Therefore, we optimized hybridization chain reaction (HCR) and microscopy for high-resolution spatial analysis of gene expression and simultaneous analysis of feeding site morphology. Here, we provide a proof of principle of the protocol, using confocal microscopy to visualize the expression of target genes Protein Phosphatase 2A subunit A3 (AtPP2AA3) and α-Expansin 4 (AtEXPA4), followed by 2-photon excitation microscopy revealing feeding site morphology. This combination enables high-resolution visualization of gene expression in a spatial context, revealing that both AtPP2AA3 and AtEXPA4 are enriched in the feeding structure. We anticipate that this combination of methods can also be applied to other (plant) studies requiring high spatial resolution and can be used to validate transcriptomic data.

Primary cilia of radial glia are important signaling compartments during embryonic brain development, but their molecular composition in vivo remains difficult to characterize. Conventional ciliary isolation can introduce substantial non-ciliary contamination, whereas APEX-based proximity labeling requires hydrogen peroxide (H₂O₂) to initiate labeling, and BioID generally requires prolonged labeling times. Here, we describe a step-by-step protocol for in vivo proximity-labeling proteomics of radial glial primary cilia in the embryonic mouse telencephalon using a Cilium-TurboID transgenic mouse model. TurboID is targeted to radial glial cilia through a ΔARL13B-GFP fusion under the BLBP promoter, enabling rapid biotin labeling in freshly dissected embryonic brain tissue. Following labeling, the tissue is subjected to subcellular fractionation, neutravidin-based enrichment of biotinylated proteins, on-bead trypsin digestion, TMT labeling, and quantitative mass spectrometry. This workflow enables spatially targeted profiling of the low-abundance ciliary proteome directly in native embryonic brain tissue, while avoiding the need to physically isolate primary cilia and eliminating the requirement for H₂O₂ during proximity labeling. The rapid labeling kinetics of TurboID also provide improved temporal resolution compared with conventional BioID, making the workflow well-suited for investigating ciliary protein composition in developing neural tissues.

Ribosomes are the central molecular machines of protein synthesis, and accurate counts of ribosome numbers are essential for linking translational capacity to cellular physiology. Common approaches infer ribosome abundance indirectly, for example, by measuring rRNA or ribosomal proteins, which can obscure spatial heterogeneity and sample-specific differences. This protocol presents an accessible image analysis pipeline for segmenting and counting individual ribosomes in three-dimensional transmission electron microscopy (3D-TEM) data. Established experimental procedures, namely high-pressure freezing (HPF), freeze substitution, resin embedding, serial semi-thick sectioning, and dual-axis tomography, are summarized here with direct references to primary protocols. The core of this work details electron tomography reconstruction and dual-axis stitching in IMOD 5.1, region-of-interest cropping in Fiji, and a semi-automated segmentation pipeline in arivis Pro 4.5. This pipeline inverts and denoises tomograms, detects ribosomal particles with a tuned Blob Finder routine, applies empirically derived voxel-count thresholds alongside a laboratory-specific shrinkage correction, and converts segmented counts to ribosomes per cubic micrometer and, for cultured cells, per cell. Validation shows that tomographic counts agree with independent biochemical rRNA measurements to within the same order of magnitude, and the protocol states the false-positive and false-negative limitations inherent to nonspecific heavy-metal staining.

Kidney organoids derived from human induced pluripotent stem cells (hiPSCs) have emerged as valuable tools for studying human kidney development, disease mechanisms, and therapeutic responses. However, widespread application of these models has been limited by variability in differentiation efficiency, organoid composition, reproducibility, and scalability across existing protocols. Robust and standardised methods are therefore needed to improve experimental consistency and facilitate wider adoption of kidney organoid systems in basic and translational research. Here, we describe an optimised and reproducible protocol for differentiating a commercially available hiPSC SCTi003-A line into kidney organoids (KiO). The protocol builds upon earlier kidney differentiation frameworks and involves directed induction of the posterior primitive streak, intermediate mesoderm specification, and subsequent three-dimensional organoid assembly. Through systematic optimisation of differentiation media, CHIR99021 concentration and timing, growth factor supplementation, and culture format, we established a standardised workflow that yields reproducible nephron and collecting duct structures. The protocol is compatible with air–liquid interface (ALI) culture and scalable suspension-based “mini-organoid” (mKiO) and monolayer culture formats that support high-content and high-throughput applications. This validated protocol provides a reliable and accessible platform for generating human KiO, addressing key limitations of reproducibility, scalability, and assay compatibility in existing approaches, and supporting future studies of kidney development, inherited kidney diseases, and preclinical drug testing.

Cryo-electron tomography (cryo-ET) is a powerful imaging technique that allows visualizing the molecular landscape within cells. In contrast to traditional preparations for electron microscopy deploying chemical fixation and contrasting agents, cryo-ET directly images cryo-fixed, i.e., vitrified samples, providing a faithful and near-native representation of cellular ultrastructure and its macromolecular constituents. While cryo-ET in combination with thinning by focused ion beam (FIB) milling is well established for single small or thin cells that can be vitrified by plunge freezing, its application to tissues is still challenging in terms of vitrification and thinning strategies. Intact plant tissues are particularly difficult, due to their relatively large cell size and the presence of vacuoles, which often occupy a large fraction of cells and usually do not vitrify. Here, we describe two methods for vitrification of the filamentous tissue (protonemata) of moss (Physcomitrium patens) by two high-pressure freezing (HPF) approaches. We also describe in detail how to prepare thin sections (lamellae) suitable for cryo-ET data acquisition by either on-grid FIB milling or lift-out with three different attachment methods that can be applied depending on skill and experimental setup of users. For the preparation of lift-out lamellae, we introduce an additional trimming step that can help to improve lamella quality. We illustrate how subcellular structures can be targeted by aiming for cell junctions using cryo-fluorescence light microscopy. The different preparation procedures can be used for samples of different sizes and other multicellular systems.

Key features

• This protocol builds on serial lift-out [1] but was adapted for moss and can be applied to other species and tissues, as described in [2].

• We describe two high-pressure freezing approaches for different specimen sizes and procedures for sample screening and targeted lamella preparation aided by cryo-fluorescence light microscopy.

• We describe on-grid lamella preparation and three lift-out approaches with a novel, more robust attachment mode.

• By applying an additional trimming step for lift-out sections, this protocol was optimized for lamella quality rather than quantity.

Leptospirosis is a widespread zoonotic disease caused by pathogenic bacteria of the genus Leptospira. The isolation and comprehensive characterization of circulating strains within a region are essential for understanding the local epidemiology and improving public health surveillance. Historically, whole blood has been the specimen of choice for isolation; however, its efficiency can be limited by the presence of inhibitory substances in the sample, and Leptospira viability may depend on rapid processing and inoculation. Here, we present an in-house culture protocol for the isolation of Leptospira from serum samples previously maintained under refrigeration (i.e., 4–8 °C) for up to 10 days. The protocol employs a real-time PCR-guided strategy by first screening specimens for the lipL32 gene. Positive samples are then inoculated into specialized EMJH media supplemented with AFAS and EMJH+AFAS supplemented with STAFF antibiotic cocktail, followed by incubation at 30 °C for up to six months. Growth is monitored weekly through visual inspection and, once turbid, the presence of Leptospira is determined via dark-field microscopy prior to downstream serogroup and genomic characterization. A significant advantage of this method is the successful recovery of viable Leptospira from non-fresh serum specimens stored under refrigeration, even in samples with low bacterial loads. Additionally, the protocol facilitates broader surveillance by repurposing serum samples already collected for routine serology, increasing the probability of identifying diverse strains without further clinical collection

Most existing preclinical models have been limited in their predictive value to mimic patients’ responses, which is a major drawback in drug development and the identification of predictive biomarkers. To overcome these limitations, patient-derived three-dimensional in vitro models have been proposed. One of them is the organoid model, which preserves the original cellular heterogeneity and recapitulates epithelial architecture and functionality. Recently, studies using patient-derived organoids for drug screening applications have increased in quantity, and organoids have already been applied to pancreatic, colon, and lung cancers and female gynecological malignancies. Here, we established a multiplex workflow to analyze longitudinal therapeutic effects of anti-cancer therapeutics on organoid growth, viability, and cytotoxicity by combining state-of-the-art viability measurement with automated live cell imaging. This workflow can be used for the prediction of patient-specific treatment response, high-throughput screening of potential anticancer drugs, and downstream analysis to identify novel therapeutic targets.