The study, published in Nature Communications by Louise Perez, Lina Shi, and colleagues in the laboratory of Patricia N. Schneider, tackles one of the deepest questions in biology: how complex organs acquire genuinely new capabilities during evolution. The vertebrate eye is often cited as an example of intricate engineering, with its layered neurons, lens, and light-sensitive photoreceptors, and understanding how such a structure can be modified without breaking it is a central challenge for evolutionary developmental biology. Anableps offers an ideal natural experiment, because the species lives its life straddling two optical environments at once. Light behaves very differently in air and in water, bending to different degrees as it enters the eye and carrying different spectral information, so a retina built for underwater vision is not automatically well suited to aerial vision.

To find out what changed in the Anableps eye, the researchers turned to two of the most powerful tools in modern genomics: single-nucleus transcriptomics and spatial transcriptomics. Single-nucleus methods allow scientists to catalog which genes are switched on in thousands of individual cells, revealing the full diversity of cell types present in a tissue. Spatial transcriptomics goes a step further by mapping where those gene-expression profiles sit within the intact organ, preserving the anatomical geography that conventional sequencing destroys. Applied to the Anableps retina, these approaches produced a cell-by-cell, position-by-position atlas of gene activity, letting the team compare the dorsal half of the retina, which handles underwater light, with the ventral half, which handles aerial light.

The atlas delivered a striking first finding: the ventral retina, the region devoted to the aerial view, is developmentally expanded. In a conventional fish eye, the dorsal and ventral halves of the retina are patterned by conserved developmental signals that establish the organ’s top-bottom axis during embryogenesis. In Anableps, the team found that markers of ventral retinal identity are overrepresented, and comparative spatial profiling of developing and adult Anableps alongside the killifish, a related surface-dwelling species with an ordinary single-vision eye, showed that the boundary separating the dorsal and ventral territories has shifted toward the dorsal side. The result is a proportionally larger ventral domain. In other words, evolution did not build a new compartment in the eye; it redrew the existing map, enlarging the aerial-vision territory at the expense of the aquatic one.

The second major finding concerns the photoreceptors themselves, the cone cells that provide color vision in bright light. Cones owe their spectral sensitivity to light-absorbing proteins called opsins, and the wavelength each opsin absorbs best is determined by its amino acid sequence. Fish genomes typically harbor multiple opsin genes, or paralogs, that arose through ancient duplications, and different copies can be tuned to different parts of the light spectrum. The spatial transcriptomic data revealed that opsin expression in Anableps is regionally organized within a single cone subtype. Cones in the ventral, aerial-vision region of the retina, designated PR1 cones, express a gene called opn1mw2, whereas the corresponding cones in the dorsal, underwater region express the longer-wavelength-sensitive opn1lw paralogs.

That regional swap matters because the two optical environments demand different sensitivities. To estimate how the proteins actually perform, the researchers used machine-learning predictions of opsin spectral properties. These analyses indicated that the Opn1mw2 pigment found in the ventral cones is red-shifted relative to Opn1mw1, another member of the medium-wavelength-sensitive opsin family, but remains blue-shifted relative to the dorsal Opn1lw pigments. The ventral retina therefore occupies an intermediate spectral position: tuned differently from the dorsal retina, and presumably better matched to the quality of light arriving from above the surface, yet still drawing on the same family of cone pigments that ancestral fishes used. The adaptation is a reshuffling of existing genetic resources, not the appearance of an entirely new photoreceptor class.

Taken together, the results sketch a coherent evolutionary pathway. Anableps did not need to evolve novel genes or novel cell types to peer into the air. Instead, it co-opted the developmental machinery that already patterns the dorsal-ventral axis of the vertebrate retina, shifting the boundary between territories so that the ventral domain grew larger, and then rewired the opsin expression programs of conserved cone subtypes so that each half of the retina is tuned to its own light environment. This kind of remodeling, in which ancient developmental circuits are redeployed to produce new proportions and new regional specializations, is increasingly recognized as a common route to evolutionary novelty, particularly in organs as complex and constrained as the eye.

The study also highlights how non-model organisms can illuminate questions that laboratory staples cannot. Anableps is not a traditional genetic model like the zebrafish or the mouse, but its unusual lifestyle makes it uniquely informative about sensory adaptation. By pairing modern transcriptomic techniques with careful comparisons against a related fish with conventional vision, the researchers could distinguish what is genuinely unusual about the Anableps retina from what is shared across teleosts. The finding that dorsal-ventral patterning markers reveal an expanded ventral identity, corroborated by the spatial comparison with killifish at both developmental and adult stages, gives the conclusion a firm anatomical footing rather than resting on gene-expression differences alone.

For the four-eyed fish itself, the payoff is a life lived at the interface. Cruising the surface with half of each eye in each world, Anableps can scan for insects and predators in the air while simultaneously watching for threats and prey below, a capability that depends on a retina partitioned into two spectrally and developmentally distinct zones. The new work shows that this partition is written into the organ’s developmental blueprint and into the pigment choices of its cone cells. It is a vivid reminder that evolution is often a tinkerer rather than an inventor, achieving dramatic functional change by enlarging, shifting, and reprogramming the parts it already has.

The research, funded by start-up funds from Louisiana State University and a grant from the National Science Foundation’s Integrative Organismal Systems program, was published as an open-access article, making the full dataset and methods available to other teams studying retinal evolution and sensory ecology. As spatial and single-cell technologies continue to spread beyond traditional model species, eyes like those of Anableps are likely to yield further secrets about how vertebrate sensory organs adapt to the world’s many light environments, one developmental tweak at a time.

Subject of Research: Evolutionary remodeling of the ventral retina enabling simultaneous aerial and aquatic vision in the four-eyed fish Anableps anableps

Article Title: Evolutionary remodeling of the ventral retina enables aerial vision in the four-eyed fish Anableps anableps

Article References: Perez, L., Shi, L., Daspit, A., Pruett, K. N., Smith, C., Prideaux, A., Sousa, J. F., & Schneider, P. N. (2026). Evolutionary remodeling of the ventral retina enables aerial vision in the four-eyed fish Anableps anableps. Nature Communications. https://doi.org/10.1038/s41467-026-78775-4

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78775-4

Keywords: four-eyed fish, Anableps anableps, retina, evolutionary developmental biology, opsins, spatial transcriptomics, single-nucleus transcriptomics, photoreceptors, aerial vision, dorsal-ventral patterning, non-model organisms, Nature Communications

Coelomocytes are the cellular workhorses of the echinoderm immune system. They circulate in the coelomic fluids that fill the body cavity, performing functions that in vertebrates are distributed among blood cells and immune cells of many kinds. For decades, biologists have classified these cells by looking at their shapes under the microscope, and sea cucumbers, or holothuroids, stand out among echinoderms for displaying the greatest variety of coelomocyte morphotypes of any class in the phylum. Yet morphology alone has proved a poor guide to function. While the overall immune role of these cells is broadly accepted, the specific jobs performed by each cell type have remained murky, and molecular data specific to the different morphotypes have been scarce in the literature.

Single-cell RNA sequencing, or scRNA-seq, has transformed how biologists untangle cellular heterogeneity, allowing researchers to profile the gene expression of thousands of individual cells and group them into transcriptionally distinct populations. The technique, however, has been largely underutilised in studies of non-model organisms, particularly among invertebrates. The new study set out to close that gap for sea cucumbers by applying the method to coelomocytes harvested from the perivisceral fluid of Holothuria forskali, a species collected with the help of the collection service of the Roscoff Biological Station in France. Sequencing was carried out at the Genomic Platform of the GIGA Institute in Liège.

The analysis identified ten distinct clusters of cells, each assumed to correspond to a separate transcriptional coelomocyte population. The clustering revealed a striking architecture: one cluster, designated cluster 0, occupied a central position relative to all the others on the two-dimensional map of gene expression, suggesting that it may represent undifferentiated cells, a reservoir of progenitors from which more specialised populations arise. At the opposite extreme, cluster 6 sat markedly apart from every other group, hinting at a fundamentally different biological identity and function.

To work out what each cluster might actually do, the team performed functional enrichment analyses and hunted for immune marker genes among the transcripts enriched in each population. The results showed that several clusters carry out key immune functions, including the recognition of pathogens, phagocytosis, the engulfing and destruction of foreign particles, the activation of complement-like pathways, and the regulation of redox balance, the delicate chemistry of oxidation and reduction that cells must manage to survive an immune response. These findings provide the first tentative clues about which transcriptional populations correspond to which defensive duties, offering a molecular handle on a system that has until now been described mostly by cell shape.

One of the most intriguing results concerns a recently discovered cell type called the carotenocyte. These cells, rich in carotenoids, the pigments responsible for many of the reds, oranges and yellows in nature, were only recently described in sea cucumbers, and their presence in the perivisceral fluid was confirmed when the researchers examined their processed samples under the microscope. By drawing on transcriptomic data previously generated for this cell type using bulk RNA sequencing, the team was able to confidently assign cluster 6, the most divergent group on the map, to the carotenocyte lineage, and to deepen the picture of what these pigment-laden cells express.

The convergence of two independent datasets proved decisive. Marker genes supported by both the bulk RNA sequencing of carotenocyte-enriched samples and the new single-cell data gave the researchers confidence in the identity of cluster 6, an approach that illustrates how single-cell and bulk methods can be combined to anchor cell identities in non-model organisms where no reference atlas exists. The study’s supplementary tables catalogue the marker genes for each cluster, their functional annotations against multiple databases, and the results of enrichment analyses against KEGG pathways and gene ontology categories, providing a resource for other researchers working on echinoderm immunity.

Rigorous quality control underpinned the analysis. The authors filtered cells on the basis of the number of unique molecular identifiers and detected genes per cell, examined the expression of mitochondrial genes to flag stressed or damaged cells, and used the DoubletFinder tool to detect and remove doublets, artefacts in which two cells are captured and sequenced as one. Sensitivity analyses testing different filtering parameters showed that while individual clusters could merge or split depending on the settings, the overall configuration of the map remained stable, with cluster 0 staying central and cluster 6 remaining the most divergent population, a reassuring sign that the biological signal is robust.

The implications reach well beyond sea cucumbers. Echinoderms are deuterostomes, the same major lineage as chordates, which makes their immune cells valuable comparators for understanding how immune cell lineages evolved on the branch of the tree that eventually produced our own adaptive immune system. A better grasp of holothuroid coelomocyte diversity could also help interpret stress responses in these animals, which are ecologically important grazers and the target of a rapidly expanding aquaculture industry in Asia. Knowing which cells respond to pathogens or environmental stressors, and through which genes, could inform disease management in farmed populations and shed light on how wild sea cucumbers cope with a changing ocean.

The authors are careful to frame the study as a pioneering first step. Linking the remaining transcriptional clusters to the morphotypes described in the historical literature will require further analyses, and the functional assignments drawn from enrichment analyses remain hypotheses to be tested experimentally. Still, the study demonstrates that single-cell transcriptomics can be successfully deployed on a non-model invertebrate with no reference-grade tools, and it delivers a preliminary but detailed map of the functional diversity of holothuroid coelomocytes. For a phylum whose immune system has been studied mostly through a microscope lens, the jump to single-cell genomics marks the beginning of a new era, one in which the hidden division of labour among sea cucumber immune cells can finally be read directly from their genes.

Subject of Research: Single-cell transcriptomic profiling of coelomocyte immune cell diversity in the sea cucumber Holothuria forskali

Article Title: Single-cell transcriptomics reveals transcriptional diversity of sea cucumber perivisceral fluid coelomocytes

Article References: Wambreuse, N., Lavergne, A., Fievez, L., Bureau, F., Zhang, L., Deng, B., Caulier, G., Eeckhaut, I., & Delroisse, J. (2026). Single-cell transcriptomics reveals transcriptional diversity of sea cucumber perivisceral fluid coelomocytes. BMC Genomics. https://doi.org/10.1186/s12864-026-13356-9

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13356-9

Keywords: sea cucumber, coelomocytes, single-cell RNA sequencing, echinoderm immunity, carotenocytes, Holothuria forskali, transcriptomics, immune cells, deuterostomes, phagocytosis, BMC Genomics, non-model organisms

The challenge the team set out to solve is a familiar one in computational biology. Predictive models trained on data from model organisms such as baker’s yeast often perform poorly when applied to species that sit far away on the tree of life. Existing datasets are heavily biased toward a handful of well-studied organisms, and this overrepresentation causes algorithms to fail in evolutionary studies of non-model species. The problem is compounded for transcriptional activators specifically, because their activation domains are intrinsically disordered, meaning they lack a fixed three-dimensional structure, and they are poorly conserved across evolution. Comparative genomics, which relies on sequence similarity to transfer functional annotations between species, simply cannot see these domains clearly.

To break through this barrier, the researchers developed a hybrid framework that pairs high-capacity molecular assays with active learning, an iterative machine learning strategy in which the model itself helps decide which experiments to run next. The centerpiece of their approach is ADhunter, a regression model designed to identify transcriptional activators and quantify how strong they are. In head-to-head benchmarking, ADhunter outperformed state-of-the-art algorithms at both tasks, distinguishing genuine activators from inactive sequences and ranking them by the intensity of the gene expression they drive.

What makes the study remarkable is the sheer scale of the evolutionary space it explores. The team deployed ADhunter’s uncertainty estimates to guide sampling across 7,842,516 proteins drawn from 2,400 fungal genomes. Rather than measuring every candidate, an impossible task even with modern robotics, the model flagged the sequences about which it was least confident, ensuring that each round of experiments delivered maximum informational value. This active learning loop, in which predictions generate experiments and experimental results refine predictions, allowed the researchers to efficiently probe the darkest corners of fungal protein space.

The experimental campaign paid off on an extraordinary scale. The team functionally characterized 9,836 activation domains from 1,071 fungal genomes, representing a 15.5-fold expansion in genome representation compared with existing datasets. For 3,416 proteins in non-model fungi, these measurements provide the first functional annotation ever recorded. Each of these domains was tested in a high-throughput molecular assay that quantifies its ability to promote gene expression, generating precisely calibrated training data rather than the noisy, indirect labels that have limited previous efforts.

Technically, the workflow begins with a training set of 17,609 protein tiles from fungal and plant proteins, each tile carrying a measured activity value. ADhunter learns the sequence-to-activity relationship from this initial dataset and then applies uncertainty sampling to select new tiles from the fungal proteomes for experimental testing. The measurements from the active learning round are harmonized onto a common activity distribution with the initial data, allowing the model to be retrained on a dataset that spans a far wider swath of evolutionary history. This closed loop of prediction, measurement, and retraining is what gives the framework its power to generalize beyond the organisms that dominated earlier datasets.

Beyond simply cataloging activators, the study probes the biophysical logic that underlies activation domain function. Interpretability analysis of ADhunter, which examines what sequence features the model relies on when making predictions, aligned with established biophysical models of how activation domains work. More intriguingly, the analysis revealed novel protein codes that had been underrepresented in previous studies, suggesting that the expanded evolutionary sampling uncovered sequence patterns that model-organism datasets had never captured. Because activation domains are disordered and rapidly evolving, these newly detected codes may represent alternative biochemical solutions that fungi have repeatedly invented to accomplish the same regulatory task.

The implications extend well beyond fungal biology. Activation domains are essential tools in synthetic biology and metabolic engineering, where researchers stitch together genetic circuits to coax microbes into producing fuels, medicines, and materials. A model that works across the fungal kingdom gives engineers a vastly larger palette of regulatory parts to choose from, including domains optimized by evolution for organisms and conditions far removed from the laboratory. The work was conducted in part through the Department of Energy’s Joint BioEnergy Institute, reflecting its relevance to bioenergy applications, and the authors include prominent synthetic biologists such as Jay Keasling alongside computational biologist Max Staller and plant and microbial biologist Patrick Shih.

Perhaps the most important message of the paper is methodological. The results highlight the importance of sampling from non-model organisms to build evolutionarily robust functional genomics models, and the framework offers a general strategy that can be adapted to other classes of disordered proteins and other branches of life. As high-throughput assays become cheaper and machine learning models grow more capable, the bottleneck in biology is increasingly the design of informative experiments. Active learning addresses exactly this bottleneck by letting the model point to where ignorance is greatest, converting limited experimental budgets into maximal biological insight.

The study also serves as a corrective to a long-standing habit in genomics. For decades, functional annotation has flowed outward from a few model organisms, leaving the overwhelming majority of life’s diversity annotated by guesswork or silence. By demonstrating that a model trained with evolutionarily guided sampling can discover and characterize functional elements that comparative methods miss entirely, the Berkeley team has made a strong case that the future of functional genomics lies in deliberately embracing diversity rather than avoiding it. The 9,836 newly characterized activation domains, and the model that found them, are now available as an open resource, inviting researchers across microbiology, evolution, and bioengineering to put the fungal kingdom’s hidden switches to work.

Subject of Research: Active learning-guided discovery and functional characterization of fungal transcriptional activation domains

Article Title: Active learning enables evolutionary discovery and characterization of fungal transcriptional activators

Article References: Waldburger, L., Nisonoff, H., Zintel, M. A., Kirkpatrick, L. D., Lam, A. W. Y., Lanclos, N., Keasling, J. D., Staller, M. V., & Shih, P. M. (2026). Active learning enables evolutionary discovery and characterization of fungal transcriptional activators. Genome Biology. https://doi.org/10.1186/s13059-026-04269-7

Image Credits: AI Generated

DOI: 10.1186/s13059-026-04269-7

Keywords: active learning, machine learning, transcriptional activators, activation domains, fungal genomics, intrinsically disordered proteins, functional genomics, ADhunter, non-model organisms, evolutionary biology, synthetic biology, uncertainty sampling

The problem rsx tackles is deceptively simple to state but computationally brutal in practice. RAD-seq allows researchers to sample thousands of genomic positions scattered across the chromosomes of organisms whose genomes have never been sequenced in full, from fish and frogs to plants and fungi. By comparing how often each of these DNA fragments appears in males versus females, scientists can flag markers that show up almost exclusively in one sex, which are candidates for the sex-determining region. The standard analysis, implemented in the RADSex tool, builds an enormous table recording the sequencing depth of every marker in every individual, then tests each row for a statistically significant sex bias. As sequencing panels grow into the millions of distinct RAD tags, that table balloons, and the original C++ implementation becomes memory-hungry to the point of failure on ordinary workstations.

rsx attacks the memory bottleneck with a set of techniques drawn from modern systems programming and database engineering. DNA sequences are compressed into 2-bit packed keys, storing four nucleotides per byte with a one-byte header that preserves the exact sequence length. After the initial parallel reading of FASTQ files, every downstream command operates either on a memory-mapped marker table or on externally sorted, lz4-compressed chunks on disk, which means the tool never copies the whole dataset into RAM. The writable memory footprint is instead bounded by the number of individuals in the study, or by an explicit user-chosen buffer, and is completely independent of how many millions of markers the panel contains. This allows researchers to analyse tables with vast numbers of rare tags without discarding candidates, a down-sampling sacrifice the memory-hungry original sometimes forced.

Speed comes from a combination of parallelism and clever data structures. Group membership is encoded in bitsets built once from the population map, with one 64-bit machine word covering 64 individuals; intersecting a marker’s presence pattern with the male and female masks then costs 64 times fewer operations than scanning individuals one by one. Producer-consumer pipelines built on crossbeam channels stream parsed records from a parser thread to per-marker consumers, and external sorting with k-way merges handles order statistics and table merging without loading everything at once. Even the median depth, a routine quality-control statistic, is computed exactly from only the nonzero entries of each depth vector, exploiting the fact that a given RAD tag is absent in most individuals, which shrinks the sorting workload dramatically.

The most scientifically consequential addition, however, is statistical rather than computational. RADSex reported only frequentist significance calls, offering no measure of how strongly the evidence actually supports sex linkage. rsx augments every marker with conjugate Beta-Binomial Bayes factors and directional posterior probabilities, computed in closed form through log-gamma functions and log-sum-exp evaluation, so no sampling or numerical quadrature is needed. Each marker is then graded into one of three explicit tiers: a strict call meeting the conventional significance threshold, a hypothesis supported by posterior evidence, or a row supported only by its Bayes factor. A configurable prior probability that any given tag is sex-linked, along with adjustable Beta prior shapes on the group prevalence rates, lets researchers tailor the evidence model to their organism. Every output row also records the exact uncorrected p-value, the Bayes factor and the posterior, together with machine-readable provenance headers documenting the parameters used.

For dimensionality reduction, rsx streams a Gram matrix accumulation rather than ever materialising the full marker-by-individual matrix. By the fundamental theorem of the singular value decomposition, the eigenvectors of the n-individual by n-individual Gram matrix are exactly the principal component loadings that a full-matrix SVD would produce, so the result is identical up to sign and numerical tolerance while memory drops from scaling with both markers and individuals to scaling only with the square of the individual count. This identity, long used for out-of-core PCA on genotype matrices, is here derived and verified with SymPy scripts and a Lean model of the median selection rule, all shipped with the code. The authors also performed a forward-error analysis of the Yates-corrected chi-squared path, showing that 64-bit integer cross-products before floating-point conversion keep reported p-values accurate to a few units in the last place, and that p-values are clamped at ten to the minus sixteen to match the original reference and avoid spurious claims of exact zeros.

Validation was rigorous and, unusually for method papers, close to exhaustive. Across four published RAD-seq panels, rsx reproduced every call made by RADSex v1.2.0, recovering all Bonferroni-significant positive-control markers, and matched the original’s six-significant-digit output formatting exactly, including a small correction step for the decimal exponent that prevents off-by-one errors on powers of ten. Precision test suites check that the p-value evaluators track the complementary error function to one part in ten to the fifteenth and never leave the valid probability interval. The optional CUDA backend batches the per-marker arithmetic on the GPU, adding up to 29.86-fold acceleration on the p-value computation batch, while Python and C bindings expose the same core to notebooks and workflow engines, closing a long-standing gap since the original offered no programmatic interface.

The authors were careful to scope their comparisons honestly. Alternative tools such as SEX-DETector, which relies on RNA-seq segregation in controlled crosses, and findZX, which uses whole-genome coverage profiles, consume fundamentally different input data and cannot be benchmarked head-to-head on identical RAD-seq panels without manufacturing measurements that were never made. The team did execute both competitors on their authors’ own example datasets, confirming installation and end-to-end operation, but stopped short of claiming relative biological accuracy. What rsx offers instead is a drop-in replacement: the complete RADSex command set is preserved, command-line compatibility is maintained, and marker-table semantics are unchanged, so existing pipelines can adopt the new engine without modification while gaining the Bayesian evidence grades as a bonus.

Released under the GPL-3.0-or-later licence with a reproducibility archive covering every reported number, rsx arrives at a moment of surging interest in sex chromosome evolution across ecology and evolutionary biology. Researchers studying species with environmental or polygenic sex determination, or tracking the early stages of sex chromosome differentiation in recently diverged lineages, now have a tool that scales to contemporary sequencing volumes on laptops rather than clusters, and that reports not just a binary verdict but a graded, transparent accounting of the evidence for every marker. For a field long constrained by the computational ceiling of its standard workflow, the arrival of an allocation-bounded, statistically richer and GPU-accelerated successor marks a genuine widening of what non-model genomics can attempt.

Subject of Research: A high-performance Rust software toolkit for identifying sex-linked markers from RAD-seq data in non-model organisms

Article Title: rsx: a high-performance streaming toolkit for RAD-seq sex determination

Article References: Goswami, R., & Goswami, R. (2026). rsx: a high-performance streaming toolkit for RAD-seq sex determination. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06628-4

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06628-4

Keywords: RAD-seq, sex determination, Rust, bioinformatics, streaming algorithms, Bayesian statistics, GPU computing, non-model organisms, sex-linked markers, BMC Bioinformatics, genomics, high-performance