Nature Protocols

Nuclei isolation from myelin-rich adult mouse brain regions remains challenging for single-nucleus RNA sequencing because myelin and debris can reduce nuclei quality. We describe an optimized protocol for mouse hippocampi and cerebella using tube-and-pestle homogenization and low-volume sucrose-gradient pelleting with a standard benchtop centrifuge, with optional magnetic enrichment of nuclei to reduce debris/non-nuclear carryover. Under the tested conditions, the workflow produces intact, debris-reduced nuclei and supports downstream 10x Genomics Flex and PARSE WT library preparation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/716374v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@ccbd87org.highwire.dtl.DTLVardef@1aef4bcorg.highwire.dtl.DTLVardef@14569a8org.highwire.dtl.DTLVardef@1bc261_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIBenchtop sucrose-gradient pelleting enables rapid nuclei purification from myelin-rich adult mouse brain C_LIO_LIScales across tissue inputs (e.g., hippocampus [~]15-20 mg; cerebellum [~]50-70 mg) without ultracentrifugation or 15 mL gradients C_LIO_LIMagnetic enrichment as the recommended final cleanup step further reduces myelin/debris carryover and is compatible with 10x Flex and PARSE WT workflows. C_LI

SummaryThe combination of target capture metagenomics and long-read sequencing represents a powerful approach for the characterisation of rare microbial taxa and their functional genes. However, standard Nanopore library preparations are incompatible with established capture protocols. A possible workaround is the preparation of Illumina libraries prior to ONT sequencing. Currently, this hybrid approach is hindered by a lack of specialised demultiplexing software capable of handling residual adapter fragments; Nanopores higher error rates and positional variability. Here, we present deluxpore: a Nextflow pipeline that demultiplexes Nanopore reads from Illumina dual-indexed libraries (NEBNext and Nextera) using BLAST alignment and Levenshtein distance matching. Extensive benchmarking across 18 replicates validates the viability and precision of this hybrid indexing approach. Benchmarking demonstrates that accurate demultiplexing requires minimum Q20 data quality and strategic index selection. Unique index-to-sample designs achieved 91.7% sample recovery at Q20 versus 46.1% for combinatorial approaches. We also identified high-crosstalk index pairs within NEBNext Primer Set A and provide an optimized 8-sample configuration achieving ~95% accuracy at Q20. deluxpore enables reliable, automated demultiplexing for hybrid capture-long-read sequencing workflows. Availability and implementationdeluxpore is implemented in Nextflow, Python, and Bash under the GNU GPL v3.0. Source code, documentation, and benchmarking workflows are available at https://github.com/compgenomicslab/deluxpore and https://github.com/compgenomicslab/deluxpore-benchmarking.

Peptergent is a novel class of amphipathic peptides that enable detergent-free extraction and purification of membrane proteins (MPs). These designed peptides self-assemble around hydrophobic transmembrane regions of proteins, forming stable, water-soluble assemblies that can be isolated directly from biological membranes. By doing so, Peptergent bypass the limitations imposed by traditional detergents, which often destabilize proteins and restrict downstream analyses. Since detergents are completely avoided, Peptergent-isolated MPs are directly amenable to structural and mass spectrometry (MS) analysis, thereby addressing their persistent underrepresentation in proteomic datasets and improving their accessibility for drug-screening strategies. Here, we describe a streamlined protocol for isolating MPs with the Peptergent PDET-1, followed by exchange into His-tagged Peptidiscs for Ni-NTA-based affinity purification. The method comprises membrane isolation, peptide preparation, protein extraction, clarification, and exchange of MPs from Peptergent to Peptidiscs. Application of this workflow yields enriched membrane proteomes compatible with downstream LC-MS/MS analysis, with improved recovery of hydrophobic and multi-pass membrane proteins. Key featuresO_LIDirect extraction and solubilization of membrane proteins in Peptergents C_LIO_LIExchange into His-tagged Peptidiscs enabling affinity purification of MPs C_LIO_LI100% detergent-free workflow compatible with LC-MS/MS analysis C_LIO_LIApplicable to cultured cells and tissue-derived membrane fractions C_LI In BriefWe describe a Peptergent-based workflow for isolating membrane proteins directly from membrane preparations. Proteins are extracted with the Peptergent peptide scaffold (PDET-1) and transferred into His-tagged Peptidisc (HD-43). The water-soluble membrane proteins are enriched by Ni-NTA affinity purification and prepared for bottom-up mass spectrometry, yielding enriched membrane proteomes and dried peptide samples ready for LC-MS analysis Graphical Overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/711971v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@af3241org.highwire.dtl.DTLVardef@c6a94org.highwire.dtl.DTLVardef@129322aorg.highwire.dtl.DTLVardef@19c7c9d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Short-read Illumina sequencing of 10x Genomics single-nucleus multiome libraries captures only the 3 end of RNA transcripts, losing transcription start site (TSS) information. Here we demonstrate nanopore sequencing of 10x multiome libraries, which enables the profiling of full length transcripts. We show concordance with common short-read sequencing based workflows including successful genetic demultiplexing of nanopore data despite its higher error rate. We compare TSS identified using nanopore sequencing of multiome cDNA to those identified using a short-read 5 assay, and provide an optimized approach for the preprocessing of nanopore reads prior to TSS identification. We find that nanopore sequencing of multiome cDNA captures a median of 63% of the TSS detected by the 5 assay.

13C-metabolic flux analysis (13C-MFA) is a crucial technique that experimentally determines metabolic flux distribution. Although precision of each flux strongly depends on tracer labeling pattern, its optimization remains challenging. We developed an integrated platform, OpenMebius2, a graphical user interface (GUI)-based software for 13C-MFA that includes a tracer labeling pattern suggestion function to support subsequent experiments. The proposed function leverages metabolic flux distributions and their 95 % confidence intervals obtained using low-cost 13C-labeled substrates to evaluate hypothetical parallel labeling scenarios and predict improvements in flux estimation precision. Availability and implementationThis software runs on Linux, macOS, and Windows. The source code and binary files are available at https://github.com/metabolic-engineering/OpenMebius2 under the PolyForm Noncommercial License 1.0.0.

This protocol enables rapid CRISPR-Cas9 genome editing in Saccharomyces cerevisiae by replacing restriction/ligation guide cloning with PCR-based protospacer installation and seamless plasmid recircularization. It describes in silico HDR donor and SgRNA design, install guide sequences into cas9 plasmid by PCR and seamless assembly, plasmid cloning and sequence verification in E. coli, and LiAc/PEG co-transformation of yeast with Cas9-sgRNA plasmid plus HDR donor. The workflow selects yeast colonies on G418 and confirms edits by PCR and sequencing.

Efficient protein synthesis in eukaryotic cells typically requires a 5' cap structure on messenger RNAs (mRNAs). However, under stress conditions or in viral infection, translation can also occur independently of the cap via internal ribosomal entry sites (IRES). IRES elements are therefore key regulators of protein expression in both viral and cellular contexts. Here we describe a cell-free protocol to quantitatively assess IRES-mediated translation using wheat germ extract (WGE) and a firefly luciferase (FLuc) reporter. The protocol includes template preparation, RNA synthesis and luminescence measurement following in vitro translation in WGE. This method enables rapid and robust comparison of IRES activity under controlled conditions and can additionally be applied to evaluate mRNA modifications designed to enhance translation efficiency. Key featuresO_LIStringent in vitro workflow from DNA template preparation through RNA synthesis and protein synthesis to reporter readout, including quality controls. C_LIO_LIEvaluation of IRES-driven translation suitable for testing combinations of IRES and CDS. C_LIO_LItranslation analysis without radioactive labeling. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/716985v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@417649org.highwire.dtl.DTLVardef@1bcd186org.highwire.dtl.DTLVardef@15fecb3org.highwire.dtl.DTLVardef@acdf8d_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical AbstractPipeline for the production and evaluation of IRES-firefly luciferase constructs using wheat germ extract. (1-4) Preparation: IRES-firefly luciferase constructs are amplified in E. coli and isolated from bacterial cells. Plasmids are linearized to prepare for in vitro transcription. (5-6) Transcript synthesis and verification: In vitro transcription is followed by electrophoretic validation to confirm integrity and correct molecular weight. (7-8) Translation and detection: Translation is executed in wheat germ extract and quantified by measuring reporter activity in a luminometer.

SummaryCentral nervous system (CNS) tumor diagnosis requires comprehensive genomic profiling including DNA-methylation classification, copy-number variants (CNV), gene fusion analysis, small variant detection and MGMT promoter methylation status. Long-read sequencing platforms such as nanopore sequencing by Oxford Nanopore Technologies and SMRTseq by PacBio can capture all these in a single assay, but integrating diverse analytical tools to leverage the advantages of long-read sequencing remains complex. We present DIANA (Diagnostic Integrated Analytics of Neoplastic Alterations), a pipeline providing fully automated end-to-end processing of long-read whole-genome sequencing data from aligned sequence reads. DIANA produces a human readable report that combines methylation classification with prioritized genetic variants to support CNS tumor diagnostics and clinical decision-making. Availability and implementationDIANA is an open-source Nextflow pipeline, freely available through Docker or Apptainer/Singularity technologies. The source code, comprehensive documentation, and installation protocols are available on GitHub: https://github.com/VilhelmMagnusLab/DIANA.git. Supplementary informationSupplementary data are available at Bioinformatics online.

Raman spectroscopy offers a uniquely rich window into molecular structure and composition, making it a powerful tool across fields ranging from materials science to biology. However, the reproducibility of Raman data analysis remains a fundamental bottleneck. In practice, transforming raw spectra into meaningful results is far from standardized: workflows are often complex, fragmented, and implemented through highly customized, case-specific code. This challenge is compounded by the lack of unified open-source pipelines and the diversity of acquisition systems, each introducing its own file formats, calibration schemes, and correction requirements. Consequently, researchers must frequently rely on manual, ad hoc reconciliation of processing steps. To address this gap, we introduce TRaP (Toolbox for Reproducible Raman Processing), an open-source, GUI-based Python toolkit designed to bring reproducibility, transparency, and portability to Raman spectral analysis. TRaP unifies the entire preprocessing-to-analysis pipeline within a single, coherent framework that operates consistently across heterogeneous instrument platforms (e.g., Cart, Portable, Renishaw, and MANTIS). Central to its design is the concept of fully shareable, declarative workflows: users can encode complete processing pipelines into a single configuration file (e.g., JSON), enabling others to reproduce results instantly without reimplementing code or reverse-engineering undocumented steps. Beyond convenience, TRaP integrates configuration management, X-axis calibration, spectral response correction, interactive processing, and batch execution into a workflow-driven architecture that enforces deterministic, repeatable operations. Every transformation is explicitly recorded, making the full processing history transparent, inspectable, and reproducible. This eliminates ambiguity in how results are generated and ensures that identical protocols can be applied consistently across datasets and experimental contexts. Through representative use cases, we show that TRaP enables seamless, reproducible preprocessing of Raman spectra acquired from diverse platforms within a unified environment. We hope TRaP can empower Raman data processing as a reproducible, shareable, and systematized scientific practice, aligning it with modern standards for computational research. TRaP is released as an open-source software at https://github.com/hrlblab/TRaP

SummaryTrack Hub Quickload Translator is a web application that interconverts University of California Santa Cruz (UCSC) Genome Browser track hub and Integrated Genome Browser (IGB) data repository formats by translating the track hub or Quickload configuration files to the other genome browsers required format. This new work enables researchers to work with tens of thousands of published genome assemblies for the first time using either browser. Availability and ImplementationTrack Hub Quickload Translator is implemented using Python 3 and freely available to use at translate.bioviz.org. Integrated Genome Browser is available from BioViz.org. Track Hub Quickload Translator, GenArk Genomes, and the Integrated Genome Browser source code is available from github.org/lorainelab. Contactaloraine@charlotte.edu

BackgroundDNA polymerase activity assays are required for enzyme quality control in biotechnology and diagnostics, but standard methods rely on specialist reagents, radioactivity and other hazardous materials, or real-time PCR instruments that are not widely accessible in resource-limited settings. This constrains local production of high quality, validated reagents and increases dependence on imported enzymes. MethodsBased on experiences derived from partnerships with scientists in several low and middle-income countries (LMICs) and stakeholder consultations, we adapted a commercial EvaGreen-based fluorometric DNA polymerase activity assay for isothermal operation using minimal equipment. Assay conditions were optimized using Design of Experiments (DOE) methodology, varying temperature, reaction volume, and MgCl2 concentration. To address reagent cost and supply-chain constraints, we developed detailed protocols for in-house synthesis of the off-patent AOAO-12 DNA dye (sold commercially as EvaGreen) and generation of single-stranded DNA templates via asymmetric PCR. ResultsOptimized isothermal assay conditions (40{degrees}C, 7.75 mM MgCl2) reliably quantified activity across multiple DNA polymerase families. In-house synthesized AOAO-12 dye exhibited comparable DNA-binding performance to commercial alternatives (R{superscript 2} = 0.95), reducing costs by more than an order of magnitude when normalized to working concentrations, enabling assay costs of approximately {pound}0.001 per reaction. The assay is effective across multiple polymerases (Bst-LF, OpenVent, Taq, Q5) and is compatible with both plate readers and qByte, a low-cost, open-source fluorometric device. ConclusionsThis stakeholder-informed assay provides an accessible, cost-effective solution for DNA polymerase quality control in resource-limited settings. The combination of optimized commercial protocols and in-house reagent synthesis offers flexibility for different resource contexts, potentially improving access to molecular biology tools globally.

BackgroundPrimary astrocyte cultures derived from neonatal rodent cortices provide a controlled system for investigating astrocyte-specific mechanisms. However, mixed glial preparations frequently contain contaminating microglia and oligodendrocyte progenitor cells, and most existing protocols require pooling tissue from multiple mouse pups to obtain sufficient astrocyte yields. This approach is impractical as it obscures sex and genotype, limits investigations of sex dependent astrocyte phenotypes, and precludes studies in certain transgenic models. To address this gap, our protocol achieves a high astrocyte yield from a single neonatal mouse brain, enabling sex- and genotype-specific cultures without the need for pooling. Mechanical removal of oligodendrocyte progenitors combined with pharmacological depletion of microglia using a Colony Stimulating Factor 1 Receptor (CSF1R) inhibitor produces highly enriched astrocytes suitable for functional assays, including those focused on sex-specific biology. MethodsCortical tissue was isolated from a single mouse pup is mechanically dissociated in astrocyte media. Cell suspensions are transferred to poly-D-lysine-coated flasks in astrocyte media. After 10-15 days in culture, OPCs are mechanically removed by horizontal shaking and microglia are selectively depleted by incubating cultures with CSF1R inhibitor PLX5622 for 24, 48, 72 and 96 hours. After PLX treatment, media is replaced and enriched astrocytes were maintained or passaged for experimentation. The sex of the pups is determined by PCR performed on DNA extracted from tail biopsies. ResultsImmunocytochemical analysis for astrocyte and microglia markers (GFAP and Iba1, respectively) showed that 24 hours of PLX5622 treatment did not fully eliminate microglia from mixed glial cultures. Extending treatment to 48 hours effectively depleted microglia while minimizing cytotoxicity and astrocyte loss and produced a pure, high-yield, sex-specific primary astrocyte culture. PCR reliably enabled the sex identification of pups used in culture using DNA extracted from tail biopsies. DiscussionThis protocol provides an efficient and reproducible method for generating high-purity, sex-specific primary astrocyte cultures from a single mouse brain. It improves consistency and purity while eliminating the need to pool tissue, preserving sex and genotype and enabling studies in transgenic mouse lines of both sexes.

Optimized histological techniques are crucial for visualizing cellular morphology across zebrafish tissues. Here, we report a rapid and reliable hematoxylin and Oil Red O (H-ORO) staining protocol for frozen sections that can be completed in less than three minutes. Mayers hematoxylin is used for nuclear staining, followed by Oil Red O (ORO) to visualize lipid-rich structures such as the endomysium surrounding myofibers, white matter of the brain, and myelin layers of major axonal tracts. Importantly, our optimized H-ORO protocol preserves tissue integrity and minimizes artifacts such as myofiber shrinkage commonly observed with ethanol-based hematoxylin and eosin (H&E) staining in both frozen and paraffin sections.

Nanopore sequencing technologies are used widely in genomics research and their adoption continues to accelerate. Basecalling is an essential step in the nanopore sequencing workflow, during which raw electrical signals are translated into nucleotide sequences. The current state-of-the-art basecaller, Oxford Nanopore Technologies (ONT) software Dorado, relies on proprietary, platform-specific NVIDIA GPU optimisations bundled in the closed-source Koi library. As a result, practical, high-speed basecalling is effectively restricted to a narrow class of supported hardware, limiting accessibility, portability, and innovation. We present (1) Openfish, an open-source GPU-accelerated nanopore basecaller decoding library that provides a competitive alternative to ONTs proprietary Koi library; and (2) Slorado, a fully open-source basecalling framework that supports both DNA and RNA with equivalent accuracy to Dorado. Together, Openfish and Slorado remove the hardware lock-in that currently limits high-performance nanopore basecalling. Our framework scales efficiently across heterogeneous computing environments, from low-power embedded devices to GPU-equipped datacenters, without sacrificing speed or accuracy. Openfish and Slorado are available as free open-source packages for basecalling research, optimisation and deployment beyond the constraints of proprietary software and hardware ecosystems: Openfish: https://github.com/warp9seq/openfish, Slorado: https://github.com/BonsonW/slorado.

Accurate fungal species identification is critical for microbial ecology, food safety, and plant pathology. However, morphological limitations and genomic complexity hinder this process. Molecular markers such as the ITS region, along with Oxford Nanopore long-read sequencing, offer a robust solution, albeit limited by error rates in homopolymeric regions and a high dependence on advanced computational resources (GPUs) to achieve high accuracy. This study benchmarks two bioinformatics workflows on a multiplexed dataset of complex fungal communities to address this technological gap: a CPU-based workflow optimized using a Bayesian machine learning engine and a GPU-accelerated workflow incorporating "super high accuracy" (SUP) models and refinement with neural networks. The results establish a scalable framework for evaluating the impact of computational architecture on final taxonomic resolution. It is demonstrated that GPU processing maximizes data retention and species-level accuracy by correcting systematic errors. Alternately, implementing automated hyperparameter optimization in CPU environments stabilizes sequence clustering and achieves high taxonomic concordance at the genus level. This conceptual advance validates the feasibility of performing ITS metabarcoding analysis in resource-constrained infrastructures, thus providing the scientific community with a reproducible protocol that balances the need for taxonomic precision with hardware availability.

Whole-genome sequencing (WGS) enables comprehensive analysis of tumour genomes, but its use in formalin-fixed paraffin-embedded (FFPE) samples is limited by DNA fragmentation and low yields. Whole-genome amplification (WGA) methods such as multiple displacement amplification (MDA) can boost DNA availability but distort copy-number alteration (CNA) profiles. DNA ligation-mediated MDA (DLMDA) mitigates this bias by reconstituting fragmented templates, yet its performance in FFPE-derived DNA remains uncertain. We compared paired DLMDA pre-amplified (2h, 8h) and non-pre-amplified FFPE prostate tumour samples from 22 archival blocks (5, 15 and 20 years old). DLMDA increased DNA yield by 42- to 86-fold, with global CNA patterns largely preserved. However, DLMDA significantly reduced the number of detected CNA deletions and amplifications. These effects were independent of both block age and reaction time. CNA dropouts were randomly distributed across the genome, indicating that DLMDA does not introduce regional bias. Our results show that DLMDA enables robust DNA yield recovery and avoids false-positive CNA artefacts, but at the cost of reduced CNA sensitivity. While suitable for CNA screening pipelines through WGS, further improvements are required to minimise the false-negative risk and improve the techniques sensitivity for FFPE-based genomics.

SummaryMitochondrial transcript abundance is a standard quality control metric in single-cell RNA sequencing, but fixed percentage thresholds fail to account for the substantial variation in mitochondrial content across cell types and tissues, risking both retention of compromised cells and exclusion of transcriptionally active viable cell populations. We present MitoChontrol, a cell-type-aware probabilistic framework for mitochondrial quality control that models the mitochondrial transcript fraction within transcriptionally coherent clusters as a Gaussian mixture distribution. Compromised-cell components are identified from the upper tail of each cluster-specific distribution, and filtering thresholds are defined as the point at which the posterior probability of cellular compromise exceeds a user-definded confidence value. Applied to controlled perturbation experiments and a pancreatic ductal adenocarcinoma single-cell dataset, MitoChontrol selectively removes transcriptionally compromised cells while preserving biologically elevated but viable populations, outperforming fixed-threshold and outlier-based approaches. Availability and ImplementationMitoChontrol is implemented in Python and integrates directly with AnnData-based workflows. It is freely available under the GNU General Public License v3 (GPL-3.0) at: https://github.com/uttamLab/MitoChontrol (DOI: https://doi.org/10.5281/zenodo.19423054)

Despite >50 years of methods development, specific antibodies are still generated at low throughput and remain in high demand across biotechnology. Most biologics and immunoprobes are monoclonal antibodies, developed using a combination of inoculating animals with a target antigen, engineered candidate libraries, and multiple rounds of selection using phage or yeast display. Here we introduce a synthetic biology scheme to eliminate the need for nearly all of these steps, by combining Surface display on E. coli and Phage display with the microvirus {Phi}X174, Assisting Continuous Evolution (SurPhACE). Instead of building libraries for screening, SurPhACE runs a closed evolutionary program. A typical experiment can have 1011 mutant candidates under active selection, with complete turnover of the mutant population every 30min, or >5x1012 unique mutants per day, using less than 100mL of bacterial culture media. We demonstrate SurPhACE for optimizing a nanobody to a related epitope, and develop novel nanobodies for an arbitrary target using a minimal starting library to establish a proof of concept and identify best practices for this scalable method for generating protein binders.

MotivationHigh-throughput sequencing generates large gene lists, making data interpretation challenging. Accurate gene annotation and reliable conversion between identifiers (e.g., gene symbols, Ensembl GeneIDs, Entrez GeneIDs) are essential for integrating datasets, conducting functional analyses, and enabling cross-species comparisons. Existing tools and databases facilitate annotation but often suffer from inconsistencies, missing mappings, and fragmented workflows, limiting reproducibility and interpretability. ResultsTo address these limitations, we developed geneslator, an R package that unifies gene identifier conversion, orthologs mapping, and pathway annotation across eight model organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, Arabidopsis thaliana). geneslator provides an up-to-date, precise, and coherent framework that preserves data integrity, enables cross-species analyses, and facilitates robust interpretation of gene function and regulation, outperforming state-of-the-art gene annotation tools. Availabilitygeneslator is available at https://github.com/knowmics-lab/geneslator. Contactgrete.privitera@unict.it

Knock-out mutants are often used to study gene function by disrupting a specific gene and comparing the mutant to a wild-type strain. Reliable interpretation, however, requires that the two strains differ only by the intended mutation and that the observed phenotype is caused specifically by the deleted gene. In the highly polyploid cyanobacterium Synechocystis sp. PCC 6803, this is particularly challenging because incomplete segregation can mask genetic heterogeneity or secondary suppressor mutations. The genetic variation among laboratory wild-type lines can further confound phenotypic analyses. We show that these challenges can be addressed by routine strain validation via whole-genome sequencing (WGS). To this end, we developed and tested user friendly workflows for short-read (Illumina), long-read (Oxford Nanopore Technologies; ONT), and hybrid data, providing standardized quality control, variant calling, and structural variant detection. We benchmarked their performance in detecting single-nucleotide polymorphisms (SNPs), small indels, and structural variants using simulated datasets across different coverages and mixed populations. Applying the workflows to three Synechocystis sp. PCC 6803 wild-type lines revealed multiple sequence and structural differences relative to the reference genome, including previously undescribed genetic variants, underscoring the importance of documenting the strain background and the value of long-read sequencing. Characterization of two independent 6-phosphogluconate dehydrogenase (gnd) knock-out mutants and their complemented strains highlighted how a failed rescue can reveal a phenotype unrelated to the intended knock-out. An automated literature analysis revealed that only a minority of the investigated Synechocystis studies that used knock-out mutants included complementation as a control (39%), whereas this practice is more common in studies involving Escherichia coli (63%) and Saccharomyces cerevisiae (55%). Based on these results, we propose a practical guide for standardizing knock-out phenotyping in Synechocystis PCC 6803. Combined with accessible workflows for routine whole-genome validation, this framework aims to support more robust and reproducible knock-out studies in the future.