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Alzheimer's disease cannot be cured as of yet. Our current understanding on the causes of Alzheimer's disease is limited. To develop treatments, experimental models that represent a particular cellular phase of the disease and more rigorous scrutiny of the cellular pathological mechanisms are crucial. In recent years, Alzheimer's disease research underwent a paradigm shift. According to this tendency, Alzheimer's disease is increasingly being conceived of a disease where not only neurons but also multiple cell types synchronously partake to manifest the pathology. Knowledge on every cell type adds an alternative approach and hope for the efforts towards the treatment. Neural stem cells and their neurogenic ability are making an appearance as a new aspect of the disease manifestation based on the recent findings that neurogenesis reduces dramatically in Alzheimer's disease patients compared to healthy individuals. Therefore, understanding how neural stem cells can form new neurons in Alzheimer's disease brains holds an immense potential for clinics. However, this provocative idea requires further evidence and tools for investigation. Recently, single cell sequencing appeared as a revolutionary tool to understand cellular programs in unprecedented resolution and it will undoubtedly facilitate comprehensive investigation of different cell types in Alzheimer's disease. In this mini-review, we will touch upon recent studies that use single cell sequencing for investigating cellular response in Alzheimer's disease and some consideration pertaining to the utilization of neural regeneration for Alzheimer's disease research.

Generating chromosome-level, haplotype-resolved assemblies of heterozygous genomes remains challenging. To address this, we developed gamete binning, a method based on single-cell sequencing of haploid gametes enabling separation of the whole-genome sequencing reads into haplotype-specific reads sets. After assembling the reads of each haplotype, the contigs are scaffolded to chromosome level using a genetic map derived from the gametes. We assemble the two genomes of a diploid apricot tree based on whole-genome sequencing of 445 individual pollen grains. The two haplotype assemblies (N50: 25.5 and 25.8 Mb) feature a haplotyping precision of greater than 99% and are accurately scaffolded to chromosome-level.

Sensory neurons are distinguished by distinct signaling networks and receptive characteristics. Thus, sensory neuron types can be defined by linking transcriptome-based neuron typing with the sensory phenotypes. Here we classify somatosensory neurons of the mouse dorsal root ganglion （DRG） by high-coverage single-cell RNA-sequencing （10 950 ±1 218 genes per neuron） and neuron size-based hierarchical clustering. Moreover, single DRG neurons responding to cutaneous stimuli are recorded using an in vivo whole-cell patch clamp technique and classified by neuron-type genetic markers. Small diameter DRG neurons are classified into one type of low-threshold mechanoreceptor and five types of mechanoheat nociceptors （MHNs）. Each of the MHN types is further categorized into two subtypes. Large DRG neurons are categorized into four types, including neurexophilin 1-expressing MHNs and mechanical nociceptors （MNs） expressing BAil-associated protein 2-like I （Baiap211）. Mechanoreceptors expressing trafficking protein particle complex 3-like and Baiap211-marked MNs are subdivided into two subtypes each. These results provide a new system for cataloging somatosensory neurons and their transcriptome databases.

The transcriptome contains rich information on molecular, cellular and organismal phenotypes. However, experimental and statistical limitations constrain sensitivity and throughput of genetic screening with single-cell transcriptomics readout. To overcome these limitations, we introduce targeted Perturb-seq (TAP-seq), a sensitive, inexpensive and platform-independent method focusing single-cell RNA-seq coverage on genes of interest, thereby increasing the sensitivity and scale of genetic screens by orders of magnitude. TAP-seq permits routine analysis of thousands of CRISPR-mediated perturbations within a single experiment, detects weak effects and lowly expressed genes, and decreases sequencing requirements by up to 50-fold. We apply TAP-seq to generate perturbation-based enhancer–target gene maps for 1,778 enhancers within 2.5% of the human genome. We thereby show that enhancer–target association is jointly determined by three-dimensional contact frequency and epigenetic states, allowing accurate prediction of enhancer targets throughout the genome. In addition, we demonstrate that TAP-seq can identify cell subtypes with only 100 sequencing reads per cell. Targeted sequencing of perturbation effects offers a sensitive approach to capture genes of interest in CRISPR-mediated screens, enabling genome-scale screens at higher scale and lower cost than whole-transcriptome Perturb-seq.

Age-associated chronic inflammation (inflammageing) is a central hallmark of ageing 1 , but its influence on specific cells remains largely unknown. Fibroblasts are present in most tissues and contribute to wound healing 2 , 3 . They are also the most widely used cell type for reprogramming to induced pluripotent stem (iPS) cells, a process that has implications for regenerative medicine and rejuvenation strategies 4 . Here we show that fibroblast cultures from old mice secrete inflammatory cytokines and exhibit increased variability in the efficiency of iPS cell reprogramming between mice. Variability between individuals is emerging as a feature of old age 5 – 8 , but the underlying mechanisms remain unknown. To identify drivers of this variability, we performed multi-omics profiling of fibroblast cultures from young and old mice that have different reprogramming efficiencies. This approach revealed that fibroblast cultures from old mice contain ‘activated fibroblasts’ that secrete inflammatory cytokines, and that the proportion of activated fibroblasts in a culture correlates with the reprogramming efficiency of that culture. Experiments in which conditioned medium was swapped between cultures showed that extrinsic factors secreted by activated fibroblasts underlie part of the variability between mice in reprogramming efficiency, and we have identified inflammatory cytokines, including TNF, as key contributors. Notably, old mice also exhibited variability in wound healing rate in vivo. Single-cell RNA-sequencing analysis identified distinct subpopulations of fibroblasts with different cytokine expression and signalling in the wounds of old mice with slow versus fast healing rates. Hence, a shift in fibroblast composition, and the ratio of inflammatory cytokines that they secrete, may drive the variability between mice in reprogramming in vitro and influence wound healing rate in vivo. This variability may reflect distinct stochastic ageing trajectories between individuals, and could help in developing personalized strategies to improve iPS cell generation and wound healing in elderly individuals. Fibroblasts from old mice are heterogeneous, which affects the ability of these fibroblasts to reprogram into induced pluripotent stem cells in vitro and influences wound healing rate in vivo.

Single-cell sequencing methods have enabled in-depth analysis of the diversity of cell types and cell states in a wide range of organisms. These tools focus predominantly on sequencing the genomes 1 , epigenomes 2 and transcriptomes 3 of single cells. However, despite recent progress in detecting proteins by mass spectrometry with single-cell resolution 4 , it remains a major challenge to measure translation in individual cells. Here, building on existing protocols 5 – 7 , we have substantially increased the sensitivity of these assays to enable ribosome profiling in single cells. Integrated with a machine learning approach, this technology achieves single-codon resolution. We validate this method by demonstrating that limitation for a particular amino acid causes ribosome pausing at a subset of the codons encoding the amino acid. Of note, this pausing is only observed in a sub-population of cells correlating to its cell cycle state. We further expand on this phenomenon in non-limiting conditions and detect pronounced GAA pausing during mitosis. Finally, we demonstrate the applicability of this technique to rare primary enteroendocrine cells. This technology provides a first step towards determining the contribution of the translational process to the remarkable diversity between seemingly identical cells. Highly sensitive ribosome profiling of single cells at single-codon resolution enables identification of distinct cell cycle-dependent translational dynamic states in individual cells.