Mesenchymal Stem Cells or Marrow Stromal Cells (MSCs) have long been viewed as a potent tool for regenerative cell therapy. MSCs are easily accessible from both healthy donor and patient tissue and expandable in vitro on a therapeutic scale without posing significant ethical or procedural problems. MSC based therapies have proven to be effective in preclinical studies for graft versus host disease, stroke, myocardial infarction, pulmonary fibrosis, autoimmune disorders and many other conditions and are currently undergoing clinical trials at a number of centers all over the world. MSCs are also being extensively researched as a therapeutic tool against neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic Lateral Sclerosis (ALS), Huntington’s disease (HD) and Multiple Sclerosis (MS). MSCs have been discussed with regard to two aspects in the context of neurodegenerative diseases: their ability to transdifferentiate into neural cells under specific conditions and their neuroprotective and immunomodulatory effects. When transplanted into the brain, MSCs produce neurotrophic and growth factors that protect and induce regeneration of damaged tissue. Additionally, MSCs have also been explored as gene delivery vehicles, for example being genetically engineered to over express glial-derived or brain-derived neurotrophic factor in the brain. Clinical trials involving MSCs are currently underway for MS, ALS, traumatic brain injuries, spinal cord injuries and stroke. In the present review, we explore the potential that MSCs hold with regard to the aforementioned neurodegenerative diseases and the current scenario with reference to the same.

Transcriptional recording by CRISPR spacer acquisition from RNA endows engineered Escherichia coli with synthetic memory, which through Record-seq reveals transcriptome-scale records. Microbial sentinels that traverse the gastrointestinal tract capture a wide range of genes and pathways that describe interactions with the host, including quantitative shifts in the molecular environment that result from alterations in the host diet, induced inflammation, and microbiome complexity. We demonstrate multiplexed recording using barcoded CRISPR arrays, enabling the reconstruction of transcriptional histories of isogenic bacterial strains in vivo. Record-seq therefore provides a scalable, noninvasive platform for interrogating intestinal and microbial physiology throughout the length of the intestine without manipulations to host physiology and can determine how single microbial genetic differences alter the way in which the microbe adapts to the host intestinal environment.

Advances in synthetic biology and microbiology have enabled the creation of engineered bacteria which can sense and report on intracellular and extracellular signals. When deployed in vivo these whole-cell bacterial biosensors can act as sentinels to monitor biomolecules of interest in human health and disease settings. This is particularly interesting in the context of the gut microbiota, which interacts extensively with the human host throughout time and transit of the gut and can be accessed from feces without requiring invasive collection. Leveraging rational engineering approaches for genetic circuits as well as an expanding catalog of disease-associated biomarkers, bacterial biosensors can act as non-invasive and easy-to-monitor reporters of the gut. Here, we summarize recent engineering approaches applied in vivo in animal models and then highlight promising technologies for designing the next generation of bacterial biosensors.

Motivation: Record-seq captures cumulative transcriptional activity over time in engineered Escherichia coli by integrating cellular RNA-derived spacer sequences into clustered regularly interspaced short palindromic repeats (CRISPR) arrays, which are read out by sequencing. Unlike the approximately uniform transcript sampling of RNA-seq, Record-seq records biological signal as spacers sampled by the CRISPR spacer acquisition machinery. Consequently, standard RNA-seq analysis strategies are not directly applicable, limiting sensitivity and interpretability. Our previous pipeline addressed these challenges only partially, retained inherited RNA-seq assumptions, and had limited algorithmic efficiency. Results: Here, we present an end-to-end computational framework for Record-seq data. To address the primary computational bottleneck of spacer sequence extraction, we implemented a wavefront alignment approach for efficient quasi-local pattern matching, achieving an approximately 30-fold speedup. We introduce transcription unit-based feature counting as an alternative to gene-body quantification to better represent prokaryotic transcription and increase statistical power by capturing signal from untranslated regions, which are spacer acquisition hotspots. For downstream analyses, we incorporate multiple normalization strategies and a nonparametric differential expression testing framework designed for sparse datasets. Further, we analyze spacer acquisition patterns and train sequence-based neural models that predict acquisition propensity from genomic sequence and annotations, providing a framework for assessing whether acquisition rules generalize as Record-seq is extended to new microbial hosts. Availability and implementation: The primary analysis workflow, the recoRdseq package, acquisition modeling repository, and relevant data are all linked at https://github.com/plattlab/Record-seq-Framework. Acquisition models and training data are on Zenodo at https://doi.org/10.5281/zenodo.18891434.