Understanding when and how cells change over time is crucial for studying biology. Traditionally, this involves continuous observation, but another method uses permanent genetic changes, like mutations, as “timestamps” to track events after they happen. Researchers developed a “molecular clock” using CRISPR technology to record the timing of cell changes along with information about cell type and lineage (family tree). This approach revealed the timing of specific cell growth during mouse development, unexpected connections between different cell types, and new states of epithelial cells based on their genetic history.��

The method was also applied to study the early stages of colon cancer in mice and humans. By analyzing 418 human precancerous polyps, researchers discovered that 15–30% originated from multiple normal cells rather than a single cell. This innovative framework combines genetic “timestamps” with single-cell analysis, providing new insights into the timing and origins of development and diseases like cancer.��

Fig. 1: Optimization of a multipurpose, single-cell capture platform.

a, gRNA capture schematic for the NSC–seq platform. The target site of gRNA scaffold anneals to NSC–seq capture sequence (CS) with a cellular barcode (blue) and unique molecular identifier (green). An additional sequence (grey) is added to the 3′-end of the complementary DNA via template switching during reverse transcription to enable downstream library amplification. This gRNA capture approach is compatible with any type of gRNA (single-guide RNA (sgRNA), hgRNA and self-targeting guide RNA) that contains the target site sequence in the scaffold (Extended Data Fig.��).��b, Cas9-induced mutation recovery by direct hgRNA capture as compared with mutations detected in DNA of the same samples.��c, gRNA capture efficiency by NSC–seq assessed in an experiment in which all cells from a drug-selected cell line should contain sgRNAs.��d, Comparative transcriptome capture efficiency between standard inDrops and NSC–seq experiments.��e, NSC–seq experiments performed on developmentally barcoded whole embryos in which Cas9 is constitutively expressed (top). Accumulative mutations on homing barcode regions increase over time (bottom),.��f, Average mutation density over embryonic time points (Extended Data Fig.��). Black dots represent geometric mean for each time point, and��P values are derived from unpaired two-tailed��t-tests.��g, Somatic mtVar calling from mitochondrial RNA (mtRNA) (top). Approach to filtering informative mtVars for lineage tracking using hgRNA mutations as ground truth (bottom) (Extended Data Fig.��).��h, Number of somatic mtVars per cell over embryonic time points. Black dot represents geometric mean for each time point, and��P values were derived from unpaired two-tailed��t-tests.��i, Pearson correlation coefficient heat map of variant proportions combining hgRNAs and mtVars for selected tissue types, presented as pseudobulk from an E9.5 embryo (Extended Data Fig.��).��j, Multimodal application of the NSC–seq platform.��a,e,g,j, Schematics created using BioRender (). a.u., arbitrary units; AUC, area under the curve; rep., replicate; prog., progenitor; bp, base pairs.��

New molecular imaging methods can capture detailed genetic and protein information directly from tissues, allowing scientists to study diseases while keeping the original structure of the tissue intact. By combining this molecular data with traditional tissue images, researchers can learn more about how different parts of tissues are affected by diseases. However, making sense of all this complex data, especially when comparing many samples, is challenging.��

To help with this, we created MILWRM, a Python tool that can quickly find and label different areas within tissue samples. MILWRM analyzes images and groups similar parts of the tissue together, making it easier to identify specific regions.��

We tested MILWRM on various tissue samples, including human colon polyps, lymph nodes, mouse kidneys, and mouse brain slices. The tool was able to distinguish different types of polyps and identify unique areas in the brain based on their molecular characteristics. MILWRM helps researchers understand the structure and molecular features of tissues, making it a valuable tool for studying diseases.��

Fig. 1: The workflow of the MILWRM pipeline.��

MILWRM begins with constructing a tissue labeler object from all the sample slides that undergo data preprocessing, serialization, and subsampling to create a randomly subsampled dataset used for k-means model construction. This subsampled data is used to find an optimal number of tissue domains, and k-selection using the adjusted inertia method. Finally, a k-means model is constructed, and each pixel is assigned a TD. Each TD has a distinct domain profile describing its molecular features. MILWRM also provides quality control metrics such as confidence scores (created with BioRender.com).��

This study aims to deepen our understanding of human brain individuality by integrating various large-scale data sets, including genomic, transcriptomic, neuroimaging, and electronic health records. The researchers used computational genomics methods to estimate genetically regulated gene expression (gr-expression) for 18,647 genes across 10 brain regions in over 45,000 people from the UK Biobank. Their analysis revealed that gr-expression patterns align with known genetic ancestry relationships, brain region identities, and gene expression correlations across different regions.

Through transcriptome-wide association studies (TWAS), they discovered 1,065 associations between gr-expression and individual differences in gray matter volumes across people and brain regions. These findings were compared to genome-wide association studies (GWAS) in the same sample, revealing hundreds of novel associations. The study also linked gr-expression to clinical phenotypes by integrating results from the ������ý���� Biobank.

Further analysis involved the Human Connectome Project (HCP), where they identified associations between polygenic gr-expression and MRI-based structural and functional brain phenotypes. The results were highly replicable, strengthening the reliability of their findings. Overall, this work offers a valuable new resource for connecting genetically regulated gene expression to brain organization and diseases, advancing our understanding of brain individuality and its clinical relevance.

Understanding the complexities of tissues and organisms is no small feat. However, scientists are making great strides with a cutting-edge technique called spatial transcriptomics (ST). This method allows us to study tissues at a microscopic level, revealing valuable information about their structure and function.But here’s the catch: analyzing and integrating data from multiple tissue slices and finding meaningful patterns within a single slice can be quite challenging. To overcome this hurdle, researchers have developed several algorithms specifically tailored for ST data analysis. These algorithms help identify distinct spatial regions within a tissue slice and align data from different sources for further analysis.

To guide researchers in choosing the right methods and paving the way for future advancements, a team of scientists conducted a comprehensive benchmarking study. They evaluated various state-of-the-art algorithms by analyzing real and simulated datasets with different sizes, technologies, species, and complexities.The researchers assessed each algorithm using a range of quantitative and qualitative metrics. These metrics included measures of clustering accuracy, visualization techniques to understand spatial relationships, alignment accuracy, and even 3D reconstruction. By considering both method performance and data quality, they provided a holistic evaluation to aid researchers in selecting the best tools for their specific needs.

The team has made all their evaluation code available on GitHub, along with online notebooks and documentation. This ensures transparency and reproducibility, allowing other researchers to validate the benchmarking results and explore new methods using different datasets.In conclusion, this groundbreaking study provides comprehensive recommendations to researchers, offering guidance in choosing optimal tools and inspiring future developments. With these advanced techniques, we are unlocking new possibilities and gaining deeper insights into the fascinating world of complex tissues.

In this study, researchers examined how our ability to control and redirect our attention develops as we grow older. They conducted their investigation by studying monkeys that were trained to disregard distractions and redirect their attention to a specific target (referred to as the antisaccade task).Through behavioral evaluations and monitoring neural activity in the prefrontal cortex, they compared the monkeys’ performance before and after puberty.

The findings revealed that adult monkeys showed significant improvements in processing the stimulus quickly, resisting the involuntary urge to look at it, and consistently following the task rules compared to when they were younger. The researchers also observed changes in the prefrontal cortex, which played a crucial role in the monkeys’ improved performance. The specific neurons in this brain region showed enhanced activity and provided neural markers of the behavioral changes, suggesting a shift from stimulus-driven to goal-driven control during each trial.

These results not only shed light on the cognitive development of monkeys but also offer important insights into how our own attentional abilities mature as we transition from adolescence to adulthood. It appears that effective allocation of attention plays a key role in achieving better response control. This study deepens our understanding of how our ability to focus and resist distractions improves over time, providing valuable knowledge about the development of cognitive skills.

In diseases like cancer, changes in our cells called copy number alterations (CNAs) are important to understand. These changes can tell us a lot about how diseases progress. Single-cell DNA sequencing (scDNA-seq) helps researchers detect CNAs in individual cells, but current tools can make mistakes across the entire genome due to wrong estimates of cell chromosome numbers, or “ploidy.”��

SCCNAInfer is a new tool designed to improve this process. It uses information from inside tumor cells to more accurately estimate each cell’s ploidy and CNAs. SCCNAInfer works alongside existing CNA detection methods by grouping cells, calculating ploidy for each group, refining the data, and accurately identifying CNAs for each cell.��

Tests show that SCCNAInfer does a better job compared to other tools like Aneufinder, Ginkgo, SCOPE, and SeCNV. This new tool can help researchers get clearer insights into cell changes, aiding in the study of cancer and other diseases.��

SCCNAInfer is freely available at .��

Researchers have systematically evaluated a range of tools designed to detect structural variants (SVs) in genomes using long-read sequencing, a method that provides more comprehensive genomic insights. The study compares 14 alignment-based methods, including advanced deep learning options, with four assembly-based methods, revealing that while assembly-based tools more effectively detect larger SVs and are robust against various testing conditions, alignment-based methods offer greater accuracy at lower sequencing coverages and excel at identifying complex SVs. This benchmarking effort helps users select the most suitable tools for different research scenarios and lays a foundation for future improvements in genomic analysis tools.