Picture a mouse embryo a few days after fertilization. It starts as a single cell and divides — 2 cells, 4, 8, 16, 32, 68 on to the blastocyst stage with more than a hundred tightly packed nuclei. If you want to understand how individual cells make fate decisions during that process — which become the embryo proper, which become extra-embryonic tissue — you need to track every nucleus across time. To do that you first have to find them all, automatically, in three-dimensional fluorescence images where the signal is deliberately kept dim to avoid killing the embryo with the very light used to image it.

That was the problem behind our 2024 paper on nuclear instance segmentation for preimplantation mouse embryos[1]. It pulled me deep into deep-learning segmentation tools, benchmarking, and a realization: no single model does well at every stage of development. More recently the same fundamental challenge resurfaced in a completely different imaging context — spatial transcriptomics on tissue sections, where images are orders of magnitude larger and cells far more heterogeneous. This is the story of both.

What “instance segmentation” actually means

Three things often get conflated. Semantic segmentation answers “where are the nuclei?” as a class label per pixel — everything is either “nucleus” or “not nucleus.” Object detection draws a bounding box around each one. Instance segmentation does both: it finds every individual nucleus and produces a precise pixel-level mask for each. That last step matters for biology. You cannot measure a nucleus’s volume, shape, or fluorescence intensity without an accurate per-nucleus mask, and a nucleus is a good proxy for cell position relative to its neighbors.

For the embryo work we were doing 3D instance segmentation on volumetric stacks acquired by selective plane illumination microscopy (SPIM) — light-sheet imaging, where a thin sheet of light illuminates one plane at a time. The embryos expressed H2B-miRFP720, a near-infrared nuclear reporter the team generated for this work. Near-infrared matters: it reduces phototoxicity, and it does not overlap spectrally with the green, red, and far-red reporters biologists typically want to image alongside it. An embryo can carry up to four distinct reporters at once.

Four problems that make this hard

The challenges are baked into the biology and the microscope physics.

• Low signal-to-noise. Light damages living embryos, so exposure is kept conservative. Foreground ends up only marginally brighter than background.
• Voxel anisotropy. A voxel — the 3D equivalent of a pixel — is not a cube. Because the sample is imaged along a single axis, z-resolution is far worse than xy-resolution. In our data the anisotropy is roughly a factor of 10.
• Increasingly dense packing. At the 16-cell stage the median surface-to-surface distance between neighboring nuclei is about 6.0 µm. By the 32-cell stage it is 2.9 µm; by 64 cells, 1.8 µm; past 100 cells, roughly 0.5 µm. Nuclei do not shrink proportionally, so they end up nearly touching.
• Variable shapes and mitosis. Nuclei divide constantly. A nucleus mid-division looks nothing like a resting one.

Seven models, one benchmark

Rather than commit to one tool and hope, the team systematically evaluated seven networks on the same ground truth. That ground truth is BlastoSPIM 1.0 — more than 570 3D images with roughly 12,000 hand-annotated nuclei, spanning the 2-cell stage to past 100 cells. Each model was trained on 482 of those images and tested on held-out data.

The seven span distinct architectural philosophies:

• Cellpose [2] — a 2D U-Net that predicts spatial gradients flowing toward each cell’s center, then clusters pixels by following those flows. Used here in 2.5D mode, processing 3D stacks slice by slice.
• Stardist-3D [3] — represents each nucleus as a star-convex polyhedron: a set of radial distances from center to boundary. A strong fit for roughly convex objects.
• QCANet [4] — two 3D U-Nets, one for semantic segmentation and one for nucleus-center detection, combined by marker-based watershed.
• RDCNet [5] — 3D recurrent dilated convolutions producing embeddings that cluster into instances.
• U3D-BCD [6] — a 3D residual U-Net jointly predicting foreground masks, instance Boundary Contours, and a signed Distance transform, combined to separate touching objects.
• UNETR-BCD [7] — swaps the encoder for a transformer stack, keeping the BCD decoder.
• ELEPHANT [8] — a 3D U-Net predicting nucleus center, periphery, and background probabilities.

Two metrics. The F1 score first asks, for each predicted nucleus, whether its overlap with a ground-truth nucleus clears an Intersection-over-Union threshold — IoU being overlap volume divided by union volume, with 0.5 the usual cutoff. Matches are binary. Panoptic quality is subtler: it sums the actual IoU values of matched pairs rather than counting them, then penalizes false positives and negatives. It rewards masks that are not merely detected but accurately shaped.

Stardist-3D won on F1 across stages, staying above 95% from the 8-cell stage through the 64-cell stage — well above prior state-of-the-art on comparable confocal data, especially past 32 nuclei. But the interesting result is where the models diverge, and why.

UNETR-BCD scored a panoptic quality roughly equal to Stardist-3D’s despite a clearly worse F1. The explanation: when UNETR-BCD does match a nucleus, the mask fits tightly — it just matches fewer of them. ELEPHANT ran the opposite way, posting a respectable F1 but persistently low panoptic quality, because it constrains every nucleus to an ellipsoid and real nuclei are not ellipsoids. And at very stringent IoU cutoffs Stardist-3D actually falls below UNETR-BCD, since a star-convex polyhedron with a fixed number of rays can only approximate a shape so far. Stardist-3D wins not by dominating every metric but by striking the right balance at the cutoff (≈0.5) that downstream tracking actually needs.

The failure modes were as informative as the scores. U3D-BCD, UNETR-BCD, and RDCNet tended to undersegment — merging two nuclei into one instance. Cellpose did the reverse, oversegmenting into small spurious instances or missing nuclei outright. QCANet usually predicted the right number of centroids, but its watershed post-processing split masks improperly when two centroids landed in the same contiguous region; it also isotropizes the image by degrading xy-resolution, which blurs the boundary between juxtaposed nuclei. Looking only at XY slices, several of these models seem fine. The XZ slices are where the anisotropy shows up.

When one model isn’t enough

Stardist-3D’s performance still deteriorated at the latest stages. So the team annotated 80 more images of late blastocysts — over 6,600 additional nuclear instances, a dataset called BlastoSPIM 2.0 — and trained a dedicated “late blastocyst” model on 72 of them.

It outperformed the original (“early embryo”) model on late blastocysts and underperformed it on embryos with fewer than 64 nuclei. A third model trained on both datasets landed in between — worse than either specialist in its own domain. The practical upshot is a switch: use the early model until the embryo reaches about 48 nuclei, then hand off to the late one. Even on the hardest test set, images with low foreground-background contrast where human annotators struggle, the early model held above 90% F1 through 64 cells and the late model reached ≈86% beyond that.

Around the time this work wrapped, a new class of models began reframing the problem. Cellpose-SAM [9] adapts Meta’s Segment Anything Model — a foundation model trained on enormous quantities of natural images — to cellular segmentation. Its authors used BlastoSPIM as one of their benchmarks. Out of the box it scored AP@0.5 IoU of 0.58 on our data. Fine-tuned on 300 images with ROIs, it reached 0.89. That is a large gain from a modest amount of additional annotation, and it hints at where the field is heading.

From embryos to tissue: a different kind of big

The embryo images span a few hundred micrometers. Segmenting cells in Xenium spatial transcriptomics data operates at another scale entirely. Xenium measures gene expression in situ — RNA transcripts detected directly within a tissue section, their positions recorded at subcellular resolution. To know which transcripts belong to which cell, you need cell segmentation. The images run roughly 30,000 × 30,000 pixels. Typical microscopy images are 1024 × 1024 or smaller.

Xenium stores images as pyramidal OME-TIFFs: the same image at nested zoom levels, each with its own pixel-to-micron factor. Level 0 is 0.2125 µm per pixel; each successive level halves the resolution, down to 6.8 µm at level 5. Level 3 sits closest to 1 µm per pixel, which makes it the best choice for overlaying transcript coordinates with minimal interpolation.

The segmentation challenges differ from the embryo case but are equally real. Tissue contains wildly different cell shapes — compact epithelial cells, sprawling macrophages, dendritic melanocytes — and a model tuned for round cells struggles with the outliers. More surprisingly, the nuclear channel creates a problem of its own. Nuclei are so bright relative to membrane and cytoplasmic staining that running Cellpose on all channels together causes it to segment nuclei, not cells. The nucleus comes out beautifully; the cell boundary, which is what you actually want, is swamped. The fix is counterintuitive: drop the nuclear channel. Run on membrane channels alone and cell morphology is preserved.

The tile-and-stitch pipeline

No GPU holds a 30k × 30k image, let alone runs Cellpose over it. The solution is tiling, which sounds trivial and is not.

Four stages. Load: read the OME-TIFF, parse its OME-XML metadata, extract the relevant channels and max-project to a 2D greyscale image. Tile: divide into 2048 × 2048 pixel tiles with 265 pixels of overlap on each shared edge — a step size of 1783 pixels. Segment: run Cellpose per tile, using cpsam (the SAM-based model), cyto3, or the dedicated nuclei model. Stitch: reassemble.

The overlap is essential. Without it, every cell straddling a tile boundary gets cut in half and produces garbage at the seam. But overlap creates its own problem — the same cell is segmented twice, once by each neighboring tile. We resolve this by centre-cropping: each tile contributes only its inner 1783 × 1783 region to the final canvas, discarding the 132–133 pixel margins where boundary effects are worst.

The second problem is label collisions. Cellpose numbers each segmented cell starting from 1. If tile (0,0) produces cells 1 through 250 and tile (0,1) also produces cells 1 through 250, they overwrite each other. Each tile’s labels are therefore offset by a fixed increment before being written to the global canvas, guaranteeing unique IDs across the whole image.

Compared against Xenium’s own default segmentation on two tissue regions, the pattern is consistent: cpsam on membrane-only channels wins. It reaches F1 around 0.75 and panoptic quality around 0.55, against 0.22–0.45 F1 for cyto3 on the same regions. The channel choice matters nearly as much as the model choice — cyto3’s F1 jumps from 0.22 to 0.40 on one region and from 0.45 to 0.66 on the other simply by dropping the nuclear channel. Precision stays high throughout (above 0.75); recall is the harder metric, which is what you would expect when finding every cell in dense, morphologically diverse tissue.

What comes next

The throughline across both projects is that the hardest part of computational image analysis is rarely the choice of architecture. It is the data engineering: knowing your image format, knowing which channels help and which dominate, understanding the physics of your microscope. Biology constrains the physics, physics constrains image quality, and image quality constrains what any algorithm can achieve.

What I am most curious about is how SAM-based models change the annotation economics. Both projects required enormous manual annotation — roughly 12,000 nuclei for BlastoSPIM 1.0, another 6,600 for 2.0, all hand-drawn by biologists and students. If Cellpose-SAM and its successors can hit high accuracy with 300 images instead of 500, that shifts how much expert labor each new imaging context demands. BlastoSPIM is now part of that ecosystem: someone else’s benchmark, used to evaluate a model we had no hand in building. That is a small but real contribution to the cumulative infrastructure that makes the next tool possible.

For anyone facing a similar problem — large images, heterogeneous cells, imperfect signal — the practical advice is this. Before reaching for a more sophisticated model, look hard at your data. Check what your channels are actually showing you. Inspect the z-slices, not just the xy views. And build your tiling carefully, because the seams are where things go wrong.

References

[1] Nunley H, Shao B, Denberg D, Grover P, Singh J, Avdeeva M, Joyce B, Kim-Yip R, Kohrman A, Biswas A, Watters A, Gal Z, Kickuth A, Chalifoux M, Shvartsman SY, Brown LM, Posfai E. Nuclear instance segmentation and tracking for preimplantation mouse embryos. Development. 2024;151(21):dev202817. doi:10.1242/dev.202817

[2] Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nature Methods. 2021;18:100–106. doi:10.1038/s41592-020-01018-x

[3] Weigert M, Schmidt U, Haase R, Sugawara K, Myers G. Star-convex polyhedra for 3D object detection and segmentation in microscopy. In: IEEE Winter Conference on Applications of Computer Vision (WACV). Snowmass, CO: IEEE; 2020:3655–3662. doi:10.1109/WACV45572.2020.9093435

[4] Tokuoka Y, Yamada TG, Mashiko D, Ikeda Z, Hiroi NF, Kobayashi TJ, Yamagata K, Funahashi A. 3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis. npj Systems Biology and Applications. 2020;6:32. doi:10.1038/s41540-020-00152-8

[5] Ortiz R, De Medeiros G, Peters AH, Liberali P, Rempfler M. RDCNet: instance segmentation with a minimalist recurrent residual network. In: Machine Learning in Medical Imaging (MLMI), MICCAI 2020 Workshop, Vol. 12436. Cham: Springer; 2020:434–443.

[6] Lin Z, Wei D, Petkova MD, Wu Y, Ahmed Z, K KS, Zou S, Wendt N, Boulanger-Weill J, Wang X, et al. NucMM dataset: 3D neuronal nuclei instance segmentation at sub-cubic millimeter scale. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021, Vol. 12901. Cham: Springer; 2021:164–174.

[7] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. UNETR: Transformers for 3D medical image segmentation. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa, HI: IEEE; 2022:1748–1758. doi:10.1109/WACV51458.2022.00181

[8] Sugawara K, Çevrim C, Averof M. Tracking cell lineages in 3D by incremental deep learning. eLife. 2022;11:e69380. doi:10.7554/eLife.69380

[9] Pachitariu M, Rariden M, Stringer C. Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv. 2025. doi:10.1101/2025.04.28.651001

By Noam Razin, Zixuan Wang, Hubert Strauss, Stanley Wei, Jason D. Lee, Sanjeev Arora

The success of Reinforcement Learning from Human Feedback (RLHF) critically depends on the quality of the reward model. However, while this quality is primarily evaluated through accuracy, it remains unclear whether accuracy fully captures what makes a reward model an effective teacher. We address this question from an optimization perspective. First, we prove that regardless of how accurate a reward model is, if it induces low reward variance, then the RLHF objective suffers from a flat landscape. Consequently, even a perfectly accurate reward model can lead to extremely slow optimization, underperforming less accurate models that induce higher reward variance. We additionally show that a reward model that works well for one language model can induce low reward variance, and thus a flat objective landscape, for another. These results establish a fundamental limitation of evaluating reward models solely based on accuracy or independently of the language model they guide. Experiments using models of up to 8B parameters corroborate our theory, demonstrating the interplay between reward variance, accuracy, and reward maximization rate. Overall, our findings highlight that beyond accuracy, a reward model needs to induce sufficient variance for efficient~optimization.

Read the paper: https://arxiv.org/abs/2503.15477

By Luis Linares-Otoya, Jaden D. Shirkey, Bhuwan Khatri Chhetri, Amira Mira, Abhishek Biswas, Samuel L. Neff, Maria V. Linares-Otoya, Ye Chen, Julio V. Campos-Florian, Mayar L. Ganoza-Yupanqui, Philip D. Jeffrey, Frederick M. Hughson & Mohamed S. Donia

Considerable advances have been made in characterizing bioactive molecules secreted by bacteria, yet the regulatory elements controlling their production remain largely understudied. Here we identify and characterize the N-acyl-cyclolysine (ACL) system—a cell-density-dependent chemical signalling system specific to and widespread in the phylum Bacteroidota (formerly Bacteroidetes)—and show that it regulates the expression of co-localized operons encoding diverse secreted molecules. Using genetic and biochemical analyses, combined with structural studies of a key biosynthetic enzyme, AclA, we elucidate the molecular structure of various ACLs and their complete biosynthetic pathway involving l-lysine acylation and ATP-dependent cyclization. Furthermore, we find that secreted ACLs are sensed by a dedicated transcription factor, AclR, resulting in the expression of associated operons and the autoinduction of ACL biosynthesis. Moreover, we show that different Bacteroidota strains produce structurally diverse ACLs and encode transcription factors with varying ligand specificities. Finally, we find that the acl circuit is widely distributed and transcribed in human gut and oral microbiome samples, with clear evidence for an active role in regulating associated operons under host colonization conditions. Understanding the function of the ACL system in different contexts has the potential to reveal details about the biology, ecology and chemistry of the Bacteroidota and how members of this phylum interact with their environments and hosts.

Read the paper: https://www.nature.com/articles/s41586-025-09418-9

By Jiayu Zhang, Elizabeth C. Chavez, Melina Winkler, Jianche Liu, Sebastian Carver, Aaron E. Lin, Abhishek Biswas, Tomokazu Tamura, Anna Tseng, Danyang Wang, Aaron Benhamou, Aoife K. O’ Connell, Mao Matsuo, Jack E. Norton, Devin Kenney, Britt Adamson, Ralph E. Kleiner, Benjamin Burwitz, Nicholas A. Crossland, Florian Douam & Alexander Ploss

The live-attenuated yellow fever 17D vaccine strain differs genetically only minimally from its virulent parent. However, it remains unclear which sequence differences lead to virulence or attenuation. Here we demonstrate, using SHAPE-MaP, that these mutations do not induce global RNA structure changes and show that protein sequence mutations are mostly responsible for the phenotypic differences between 17D and virulent YFV. Using a highly modular, combinatorial genetic approach, we identified key mutations in the envelope (E) and non-structural 2A (NS2A) proteins that increase 17D’s ability to spread and enhance host antiviral responses. Introducing these mutations into infectious clones of virulent YFV genomes results in viral attenuation in vitro and in two mouse models. Collectively, our results define the genetic basis for 17D attenuation and highlight a potentially general approach for creating live-attenuated vaccines by introducing mutations resulting in similar phenotypic changes in other pathogenic viruses.

Read the paper: https://www.nature.com/articles/s41564-025-02047-y

By Sofia A. Quinodoz, Lifei Jiang, Aya A. Abu-Alfa, Troy J. Comi, Hongbo Zhao, Qiwei Yu, Lennard W. Wiesner, Jordy F. Botello, Anita Donlic, Elizabeth Soehalim, Prashant Bhat, Christiane Zorbas, Ludivine Wacheul, Andrej Košmrlj, Denis L. J. Lafontaine, Sebastian Klinge & Clifford P. Brangwynne

Biomolecular condensates are key features of intracellular compartmentalization. As the most prominent nuclear condensate in eukaryotes, the nucleolus is a multiphase liquid-like structure in which ribosomal RNAs (rRNAs) are transcribed and processed, undergoing multiple maturation steps to form the small (SSU) and large (LSU) ribosomal subunits. However, how rRNA processing is coupled to the layered organization of the nucleolus is poorly understood owing to a lack of tools to precisely monitor and perturb nucleolar rRNA processing dynamics. Here we developed two complementary approaches to spatiotemporally map rRNA processing and engineer de novo nucleoli. Using sequencing in parallel with imaging, we found that rRNA processing steps are spatially segregated, with sequential maturation of rRNA required for its outward movement through nucleolar phases. By generating synthetic nucleoli in cells using an engineered rDNA plasmid system, we show that defects in SSU processing can alter the ordering of nucleolar phases, resulting in inside-out nucleoli and preventing rRNA outflux, while LSU precursors are necessary to build the outermost layer of the nucleolus. These findings demonstrate how rRNA is both a scaffold and substrate for the nucleolus, with rRNA acting as a programmable blueprint for the multiphase architecture that facilitates assembly of an essential molecular machine.

Read the paper: https://www.nature.com/articles/s41586-025-09207-4

By Naman Agarwal, Xinyi Chen, Evan Dogariu, Devan Shah, Hubert Strauss, Vlad Feinberg, Daniel Suo, Peter Bartlett, Elad Hazan

We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill, a general-purpose fast generation method for any sequence prediction algorithm based on convolutional operators. FutureFill reduces generation time from quadratic to quasilinear in the context length. Moreover, when generating from a prompt, it requires a prefill cache whose size grows only with the number of tokens to be generated, often much smaller than the caches required by standard convolutional or attention based models. We validate our theoretical claims with experiments on synthetic tasks and demonstrate substantial efficiency gains when generating from a deep convolutional sequence prediction model.

Read the paper: https://arxiv.org/abs/2410.03766

By Ted Zadouri, Hubert Strauss, and Tri Dao

LLM decoding is bottlenecked for large batches and long contexts by loading the key-value (KV) cache from high-bandwidth memory, which inflates per-token latency, while the sequential nature of decoding limits parallelism. We analyze the interplay among arithmetic intensity, parallelization, and model quality and question whether current architectures fully exploit modern hardware. This work redesigns attention to perform more computation per byte loaded from memory to maximize hardware efficiency without trading off parallel scalability. We first propose Grouped-Tied Attention (GTA), a simple variant that combines and reuses key and value states, reducing memory transfers without compromising model quality. We then introduce Grouped Latent Attention (GLA), a parallel-friendly latent attention paired with low-level optimizations for fast decoding while maintaining high model quality. Experiments show that GTA matches Grouped-Query Attention (GQA) quality while using roughly half the KV cache and that GLA matches Multi-head Latent Attention (MLA) and is easier to shard. Our optimized GLA kernel is up to 2x faster than FlashMLA, for example, in a speculative decoding setting when the query length exceeds one. Furthermore, by fetching a smaller KV cache per device, GLA reduces end-to-end latency and increases throughput in online serving benchmarks by up to 2x.

Read the paper: https://arxiv.org/abs/2505.21487

By Lindsay A. Becker, Sofia A. Quinodoz, Troy J. Comi, Ofer Kimchi, David A. Knowles, and Clifford P. Brangwynne

Subcellular RNA organization can affect critical cellular functions. However, our understanding of RNA microenvironments, particularly biomolecular condensates, remains limited, largely due to a lack of technologies to comprehensively interrogate mesoscale RNA organization. Here, we adapt Split-Pool Recognition of Interactions by Tag Extension to map micron-scale RNA-RNA spatial proximity genome-wide across cell regions (RNA-SPRITE). Deploying RNA-SPRITE, we find extensive, conserved organization of mature mRNAs, with increased colocalization between mRNAs that share RNA-binding protein (RBP) motifs or encode functionally related proteins. Both effects are especially strong in dendrites and axons, suggesting prevalent mRNA co-regulation. Moreover, mRNAs with less compact folding, lower translation efficiency, and specific RBP motifs are more likely to be in RNA-rich condensates. However, perturbations that broadly dissolve or enhance condensation reveal that RBP motif and encoded protein-mediated colocalizations largely remain intact, independent of condensation. These results demonstrate the power of RNA-SPRITE in revealing critical aspects of RNA’s functional organization.

In Brief Unbiased, genome-wide maps of RNA-RNA mesoscale spatial proximity uncover extensive subcellular organization and its governing principles.

Highlights

• RNA-SPRITE reveals micron-scale RNA colocalization genome-wide across cell regions
• mRNA colocalization specificity is driven by shared motifs and encoded protein function
• mRNAs with less compact folding, lower translation efficiency, and distinct protein-binding motifs are more likely to be in condensates
• Neurites have a particularly high degree of sequence and function-dependent mRNA organization

Read the paper: https://www.biorxiv.org/content/10.1101/2025.04.19.649570v1

By Mohamed Abdellatif, Joel U. Bretheim, and Marina Rustow

Different writing systems have been (historically and contemporarily) used to write out the same language. This is typically done by substituting letters (or symbols, in the case of non-alphanumeric systems). However, depending on the language and the involved writing systems, the process may not be purely deterministic. Quoting Becker and Becker [2000]

even such basic acts as transliteration involve interpretation– to the extent that there is
meaning in the medium itself
.
.
In transliteration itself there is exuberance (that is, meaning is added) and deficiency
(meaning is lost).

This gives significance to the problem of Machine Translation in the intersection of Digital Humanities and Natural Language Understanding. Transformer-based models achieved success modeling human languages. However, many of them have the limitation of handling an input of maximum length of 512 tokens. To reuse a pre-trained model with this limitation for downstream tasks (e.g., Machine Transliteration) on input of sequences longer than 512 tokens, we propose a method to segment the input into interleaving (not mutually exclusive) pieces, invoke the model in a piecewise manner and construct the result. To consolidate the result, we propose a method to detect and correct potential (duplication and elimination) errors that reduces Word Error Rate from 0.0985 to 0.0.

Read the paper: https://zenodo.org/records/14982300

By Henri Schmidt, Minsi Zhang, Dimitar Chakarov, Vineet Bansal, Haralambos Mourelatos, Francisco J. Sánchez-Rivera, Scott W. Lowe, Andrea Ventura, Christina S. Leslie & Yuri Pritykin

We present GuideScan2 for memory-efficient, parallelizable construction of high-specificity CRISPR guide RNA (gRNA) databases and user-friendly design and analysis of individual gRNAs and gRNA libraries for targeting coding and non-coding regions in custom genomes. GuideScan2 analysis identifies widespread confounding effects of low-specificity gRNAs in published CRISPR screens and enables construction of a gRNA library that reduces off-target effects in a gene essentiality screen. GuideScan2 also enables the design and experimental validation of allele-specific gRNAs in a hybrid mouse genome. GuideScan2 will facilitate CRISPR experiments across a wide range of applications.

Read the paper: https://doi.org/10.1186/s13059-025-03488-8