Abstract The diversity of T-cell receptors (TCRs) and B-cell receptors (BCRs) underpins the adaptive immune system’s ability to recognize and respond to a wide array of antigens. Recent advancements in RNA sequencing have expanded its application beyond transcriptomics to include the analysis of immune repertoires, enabling the exploration of TCR and BCR sequences across various physiological and pathological contexts. This review highlights key methodologies and considerations for TCR and BCR repertoire analysis, focusing on the technical aspects of receptor sequence extraction, data processing, and clonotype identification. We compare the use of bulk and single-cell sequencing, discuss computational tools and pipelines, and evaluate the implications of examining specific receptor regions such as CDR3. By integrating immunology, bioinformatics, and clinical research, immune repertoire analysis provides valuable insights into immune function, therapeutic responses, and precision medicine approaches, advancing our understanding of health and disease. <<<

Over time, cells in the brain and in the body accumulate damage, which contributes to the ageing process. In the human brain, the prefrontal cortex undergoes age-related changes that can affect cognitive functioning later in life. Here, using single-nucleus RNA sequencing (snRNA-seq), single-cell whole-genome sequencing (scWGS) and spatial transcriptomics, we identify gene-expression and genomic changes in the human prefrontal cortex across lifespan, from infancy to centenarian. snRNA-seq identified infant-specific cell clusters enriched for the expression of neurodevelopmental genes, as well as an age-associated common downregulation of cell-essential homeostatic genes that function in ribosomes, transport and metabolism across cell types. Conversely, the expression of neuron-specific genes generally remains stable throughout life. These findings were validated with spatial transcriptomics. scWGS identified two age-associated mutational signatures that correlate with gene transcription and gene repression, respectively, and revealed gene length- and expression-level-dependent rates of somatic mutation in neurons that correlate with the transcriptomic landscape of the aged human brain. Our results provide insight into crucial aspects of human brain development and ageing, and shed light on transcriptomic and genomic dynamics. <<<

Mingzhi Luo, Xiangrong Zhang, Jia Guo, Rong Gu, Youyuan Qin, Kai Ni, Lei Liu, Yan Pan, Jingjing Li, Honglei Shi, Linhong Deng <<<

Abstract Embryonic development follows a conserved sequence of events across species, yet the pace of development is highly variable and particularly slow in humans. Species-specific developmental timing is largely recapitulated in stem cell models, suggesting a cell-intrinsic clock. Here we use directed differentiation of human embryonic stem cells into neuroectoderm to perform a whole-genome CRISPR-Cas9 knockout screen and show that the epigenetic factors Menin and SUZ12 modulate the speed of PAX6 expression during neural differentiation. Genetic and pharmacological loss-of-function of Menin or SUZ12 accelerate cell fate acquisition by shifting the balance of H3K4me3 and H3K27me3 at bivalent promoters, thereby priming key developmental genes for faster activation upon differentiation. We further reveal a synergistic interaction of Menin and SUZ12 in modulating differentiation speed. The acceleration effects were observed in definitive endoderm, cardiomyocyte and neuronal differentiation paradigms, pointing to chromatin bivalency as a general driver of timing across germ layers and developmental stages. <<<

Nadine Serhan, Nasser S. Abdullah, Nadine Gheziel, Alexia Loste, Rüçhan Ekren, Elodie Labit, Anne-Alicia Gonzalez, Giulia Oliva, Pauline Tarot, Camille Petitfils, Gaëlle Payros, Paolo D’Avino, Allison Voisin, Holly Freya Grace Tinsley, Rebecca Gentek, Carole Brosseau, Marie Bodinier, Laurent Reber, Pierre Val, Cezmi A. Akdis, Yasutaka Mitamura, Anand Kumar Andiappan, Jerry Kok Yen Chan, Florent Ginhoux, Amaury François, Nicolas Cénac, Lilian Basso, Nicolas Gaudenzio <<<

A central component of wayfinding is the ability to maintain a consistent representation of one's facing direction (FD) when moving about the world. In rodents, head direction cells are believed to support this “neural compass,” but identifying a similar mechanism in humans during dynamic naturalistic navigation has been challenging. To address this issue, we acquired fMRI data from male and female human participants while they freely navigated through a virtual reality city. Encoding model analyses revealed voxel clusters in the posterior–medial cortex (the “retrosplenial complex”) and superior parietal lobule that exhibited reliable tuning as a function of FD. Crucially, these directional tunings were consistent across perceptually different versions of the city, spatially separated locations within the city, and motivationally distinct phases of the behavioral task. Analysis of the model weights indicated that these regions represent a broad range of possible FDs and that they do so by representing heading relative to the principal axis of the environment. These findings reveal specific mechanisms in the human brain that allow us to maintain a sense of direction during naturalistic, dynamic navigation. <<<

Frederic Tremblay, Qiang Xiong, Shrijal S. Shah, Chih-Wei Ko, Kenneth Kelly, Mary S. Morrison, Cristiana Giancarlo, Ricardo N. Ramirez, Erica M. Hildebrand, Sarah B. Voytek, Gabriel K. El Sebae, Shane H. Wright, Liam Lofgren, Scott Clarkson, Christine Waters, Samantha J. Linder, Songlei Liu, Taesun Eom, Shefal Parikh, Yuki Weber, Salette Martinez, Padma Malyala, Sahar Abubucker, Ari E. Friedland, Morgan L. Maeder, Angelo Lombardo, Vic E. Myer, Aron B. Jaffe <<<

Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift -- from scaling static models to developing self-evolving agents -- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organized around three foundational dimensions -- what to evolve, when to evolve, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing adaptive agentic systems in both research and real-world deployments, ultimately shedding lights to pave the way for the realization of Artificial Super Intelligence (ASI), where agents evolve autonomously, performing at or beyond human-level intelligence across a wide array of tasks. <<<

Jiale Xiang, Xiangzhong Sun, Jiguang Peng, Hongfu Zhang, Jiankun Shen, Jingrou Li, Hongyu Li, Lanping Hu, Jingjing Zhang, Shihao Zhou, Sihu Xu, Yun Yang, Jun He, Zhiyu Peng <<<

Jiaqi You, Zhenping Liu, Zhengyang Qi, Yizan Ma, Mengling Sun, Ling Su, Hao Niu, Yabing Peng, Xuanxuan Luo, Mengmeng Zhu, Yuefan Huang, Xing Chang, Xiubao Hu, Yuqi Zhang, Ruizhen Pi, Yuqi Liu, Qingying Meng, Jianying Li, Qinghua Zhang, Longfu Zhu, Zhongxu Lin, Ling Min, Daojun Yuan, Corrinne E. Grover, David D. Fang, Keith Lindsey, Jonathan F. Wendel, Lili Tu, Xianlong Zhang, Maojun Wang <<<

AbstractPolyploidy complicates transcriptional regulation and increases phenotypic diversity in organisms. The dynamics of genetic regulation of gene expression between coresident subgenomes in polyploids remains to be understood. Here we document the genetic regulation of fiber development in allotetraploid cotton Gossypium hirsutum by sequencing 376 genomes and 2,215 time-series transcriptomes. We characterize 1,258 genes comprising 36 genetic modules that control staged fiber development and uncover genetic components governing their partitioned expression relative to subgenomic duplicated genes (homoeologs). Only about 30% of fiber quality-related homoeologs show phenotypically favorable allele aggregation in cultivars, highlighting the potential for subgenome additivity in fiber improvement. We envision a genome-enabled breeding strategy, with particular attention to 48 favorable alleles related to fiber phenotypes that have been subjected to purifying selection during domestication. Our work delineates the dynamics of gene regulation during fiber development and highlights the potential of subgenomic coordination underpinning phenotypes in polyploid plants. <<<

The Mycobacterium tuberculosis complex (MTBC) is a group of bacteria causing tuberculosis (TB) in humans and animals. Understanding MTBC genetic diversity is crucial for insights into its adaptation and traits related to survival, virulence, and antibiotic resistance. While it is known that within-MTBC diversity is characterised by large deletions found only in certain lineages (regions of difference [RDs]), a comprehensive pangenomic analysis incorporating both coding and non-coding regions remains unexplored. We utilised a curated dataset representing various MTBC genomes, including under-represented lineages, to quantify the full diversity of the MTBC pangenome. The MTBC was found to have a small, closed pangenome with distinct genomic features and RDs both between lineages (as previously known) and between sub-lineages. The accessory genome was identified to be a product of genome reduction, showing both divergent and convergent deletions. This variation has implications for traits like virulence, drug resistance, and metabolism. The study provides a comprehensive understanding of the MTBC pangenome, highlighting the importance of genome reduction in its evolution, and underlines the significance of genomic variations in determining the pathogenic traits of different MTBC lineages. <<<

This paper introduces Genesis, the first compiler designed to supportHamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable(DV) quantum computing systems. Genesis is a two-level compilation system. Atthe first level, it decomposes an input Hamiltonian into basis gates using thenative instruction set of the target hybrid CV-DV quantum computer. At thesecond level, it tackles the mapping and routing of qumodes/qubits to implementlong-range interactions for the gates decomposed from the first level. Ratherthan a typical implementation that relies on SWAP primitives similar toqubit-based (or DV-only) systems, we propose an integrated design ofconnectivity-aware gate synthesis and beamsplitter SWAP insertion tailored forhybrid CV-DV systems. We also introduce an OpenQASM-like domain-specificlanguage (DSL) named CVDV-QASM to represent Hamiltonian in terms ofPauli-exponentials and basic gate sequences from the hybrid CV-DV gate set.Genesis has successfully compiled several important Hamiltonians, including theBose-Hubbard model, $\mathbb{Z}_2-$Higgs model, Hubbard-Holstein model,Heisenberg model and Electron-vibration coupling Hamiltonians, which arecritical in domains like quantum field theory, condensed matter physics, andquantum chemistry. Our implementation is available atGenesis-CVDV-Compiler(https://github.com/ruadapt/Genesis-CVDV-Compiler). <<<

Akanksha Jain, Gilles Gut, Fátima Sanchis-Calleja, Reto Tschannen, Zhisong He, Nicolas Luginbühl, Fides Zenk, Antonius Chrisnandy, Simon Streib, Christoph Harmel, Ryoko Okamoto, Malgorzata Santel, Makiko Seimiya, René Holtackers, Juliane K. Rohland, Sophie Martina Johanna Jansen, Matthias P. Lutolf, J. Gray Camp, Barbara Treutlein <<<

Abstract Brain organoids enable the mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems1–3. Here we establish long-term, live light-sheet microscopy on unguided brain organoids generated from fluorescently labelled human induced pluripotent stem cells, which enables tracking of tissue morphology, cell behaviours and subcellular features over weeks of organoid development4. We provide a novel dual-channel, multi-mosaic and multi-protein labelling strategy combined with a computational demultiplexing approach to enable simultaneous quantification of distinct subcellular features during organoid development. We track actin, tubulin, plasma membrane, nucleus and nuclear envelope dynamics, and quantify cell morphometric and alignment changes during tissue-state transitions including neuroepithelial induction, maturation, lumenization and brain regionalization. On the basis of imaging and single-cell transcriptome modalities, we find that lumenal expansion and cell morphotype composition within the developing neuroepithelium are associated with modulation of gene expression programs involving extracellular matrix pathway regulators and mechanosensing. We show that an extrinsically provided matrix enhances lumen expansion as well as telencephalon formation, and unguided organoids grown in the absence of an extrinsic matrix have altered morphologies with increased neural crest and caudalized tissue identity. Matrix-induced regional guidance and lumen morphogenesis are linked to the WNT and Hippo (YAP1) signalling pathways, including spatially restricted induction of the WNT ligand secretion mediator (WLS) that marks the earliest emergence of non-telencephalic brain regions. Together, our work provides an inroad into studying human brain morphodynamics and supports a view that matrix-linked mechanosensing dynamics have a central role during brain regionalization. <<<

Wen-Yan Cheng, Jian-Feng Li, Yong-Mei Zhu, Xiang-Jie Lin, Li-Jun Wen, Fan Zhang, Yu-Liang Zhang, Ming Zhao, Hai Fang, Sheng-Yue Wang, Xiao-Jing Lin, Niu Qiao, Wei Yin, Jia-Nan Zhang, Yu-Ting Dai, Lu Jiang, Xiao-Jian Sun, Yi Xu, Tong-Tong Zhang, Su-Ning Chen, Hong-Hu Zhu, Zhu Chen, Jie Jin, De-Pei Wu, Yang Shen, Sai-Juan Chen <<<

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. Robust identification of clinically and biologically relevant molecular subtypes from nongenomic high-throughput sequencing data remains challenging. We established the largest multicenter AML cohort (n = 655) in China, with all patients subjected to RNA sequencing (RNA-Seq) and 619 (94.5%) to targeted or whole-exome sequencing (TES/WES). Based on an enhanced consensus clustering, eight stable gene expression subgroups (G1–G8) with unique clinical and biological significance were identified, including two unreported (G5 and G8) and three redefined ones (G4, G6, and G7). Apart from four well-known low-risk subgroups including PML::RARA (G1), CBFB::MYH11 (G2), RUNX1::RUNX1T1 (G3), biallelic CEBPA mutations or -like (G4), four meta-subgroups with poor outcomes were recognized. The G5 (myelodysplasia-related/-like) subgroup enriched clinical, cytogenetic and genetic features mimicking secondary AML, and hotspot mutations of IKZF1 (p.N159S) (n = 7). In contrast, most NPM1 mutations and KMT2A and NUP98 fusions clustered into G6–G8, showing high expression of HOXA / B genes and diverse differentiation stages, from hematopoietic stem/progenitor cell down to monocyte, namely HOX -primitive (G7) , HOX -mixed (G8), and HOX -committed (G6). Through constructing prediction models, the eight gene expression subgroups could be reproduced in the Cancer Genome Atlas (TCGA) and Beat AML cohorts. Each subgroup was associated with distinct prognosis and drug sensitivities, supporting the clinical applicability of this transcriptome-based classification of AML. These molecular subgroups illuminate the complex molecular network of AML, which may promote systematic studies of disease pathogenesis and foster the screening of targeted agents based on omics. <<<

Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Raut Altae-Tran, Magnus Bauer, Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, David Baker <<<

AbstractDe novoenzyme design starts from ideal active site descriptions consisting of constellations of catalytic residue functional groups around reaction transition state(s), and seeks to generate protein structures that can accurately hold the site in place. Highly active enzymes have been designed starting from such descriptions using the generative AI method RFdiffusion [1–3], but there are two current methodological limitations. First, the geometry of the active site can only be specified at the residue level, so for each catalytic residue functional group placed around the reaction transition state, the possible locations of the residue backbone must be enumerated by building side chain rotamers back from the functional group. Second, the location of the catalytic residues along the sequence must be specified in advance, which considerably limits the space of solutions which can be sampled. Here we describe a new deep generative method, Rosetta Fold diffusion 2 (RFdiffusion2), that solves both problems, enabling enzymes to be designed from sequence agnostic descriptions of functional group locations without inverse rotamer generation. We first evaluate RFdiffusion2 on anin silicoenzyme design benchmark of 41 diverse active sites and find that it is able to successfully build proteins scaffolding all 41 sites, compared to 16/41 with prior state-of-the-art deep learning methods. Next, we design enzymes around three diverse catalytic sites and characterize the designs experimentally; in each case we identify active catalysts in testing less than 96 sequences. RFdiffusion2 demonstrates the potential of atomic resolution generative models for the design ofde novoenzymes directly from their reaction mechanisms. <<<

Gayani Senevirathne, Serena C. Fernandopulle, Daniel Richard, Stephanie L. Baumgart, Anika Liv Christensen, Matteo Fabbri, Jakob Höppner, Harald Jüppner, Peishu Li, Vivien Bothe, Nadia Fröbisch, Ian Simcock, Owen J. Arthurs, Alistair Calder, Naomi Freilich, Niamh C. Nowlan, Ian A. Glass, April Craft, Terence D. Capellini <<<