Single Tissue Research Articles

Fluorescence Lifetime Multiplexing (FLEX) for simultaneous high dimensional spatial biology in 3D

Immunohistochemistry is a crucial method for detecting specific proteins within tissue samples, yet constrained to one biomarker per tissue section. Multiplexed immunofluorescence, while allowing simultaneous visualization of multiple proteins, faces limitations in the number of simultaneous fluorescent labels due to spectral overlap. Although cyclic immunofluorescence techniques have successfully broadened antibody staining capacities in a single tissue sample, they are plagued by time-consuming and labor-intensive procedures, sample degradation risks, and inability to scale beyond thin sections. In this study, we introduce the use of 3D confocal Fluorescence Lifetime Imaging Microscopy as a high-throughput, multiplexed immunofluorescence platform that can differentiate 11 or more biomarkers in 3D tissue volumes. Leveraging both spectral and lifetime information, this approach allows for practical spatial biology in thin sections that can readily scale to larger volumes of tissue. We believe that this highly multiplexed and versatile biomarker imaging platform will significantly expedite cancer research and enable new translational approaches in the future.

Secondary integrated analysis of multi-tissue transcriptomic responses to a combined lifestyle intervention in older adults from the GOTO nonrandomized trial

Molecular effects of lifestyle interventions are typically studied in a single tissue. Here, we perform a secondary analysis on the sex-specific effects of the Growing Old TOgether trial (GOTO, trial registration number GOT NL3301 (https://onderzoekmetmensen.nl/nl/trial/27183), NL-OMON27183, primary outcomes have been previously reported in ref. 1), a moderate 13-week combined lifestyle intervention on the transcriptomes of postprandial blood, subcutaneous adipose tissue (SAT) and muscle tissue in healthy older adults, the overlap in effect between tissues and their relation to whole-body parameters of metabolic health. The GOTO intervention has virtually no effect on the postprandial blood transcriptome, while the SAT and muscle transcriptomes respond significantly. In SAT, pathways involved in HDL remodeling, O2/CO2 exchange and signaling are overrepresented, while in muscle, collagen and extracellular matrix pathways are significantly overexpressed. Additionally, we find that the effects of the SAT transcriptome closest associates with gains in metabolic health. Lastly, in males, we identify a shared variation between the transcriptomes of the three tissues. We conclude that the GOTO intervention has a significant effect on metabolic and muscle fibre pathways in the SAT and muscle transcriptome, respectively. Aligning the response in the three tissues revealed a blood transcriptome component which may act as an integrated health marker for metabolic intervention effects across tissues.

Real-time in vivo confocal laser endomicroscopic imaging of equine endometrium: Preliminary observations and feasibility study

Endometrial health is vital for the reproductive efficiency of broodmares and accurate diagnostic testing is crucial for directing the best treatment options and outcomes. Confocal laser endomicroscopy (CLE) is an endoscopic technique for obtaining in-vivo, real-time microscopic imaging of tissues using a fiber optic probe. CLE relies on induced tissue fluorescence and fluorescein sodium, given intravenously, is the contrast agent most used in human medicine. This study aimed to determine the feasibility of CLE for imaging equine endometrium and determine a standard dose of fluorescein sodium to achieve optimal cellular imaging. In-vivo CLE was performed on 44 mares, and the images were compared with routine histopathological analysis of endometrial biopsies. No adverse reactions occurred after IV fluorescein sodium administration and a dose of 4 mg/kg was established (0.04 mL/kg of 10 % fluorescein sodium solution) to achieve optimal image contrast. CLE enabled multiple regions of the endometrium to be assessed quickly. Distinct tissue architecture patterns could be appreciated using CLE, and the luminal epithelium could be assessed for integrity (ulceration) and exocytosed inflammatory cells. Endometrial gland distribution, density, shape, and epithelial height were evaluated. Blood vessels were clearly outlined, and inflammatory cells and fibrosis were discernable within the interstitium. Image quality varied between mares, and the stage of oestrous cycle may have been a factor of influence. This novel imaging modality enables collection of “virtual” biopsies and facilitates critical assessment of multiple regions of the uterus compared with the standard histopathologic assessment of a single random tissue biopsy.

Benchmarking clustering, alignment, and integration methods for spatial transcriptomics

BackgroundSpatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development.ResultsIn this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets.ConclusionsOur analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.

DNA methylation correlates of chronological age in diverse human tissue types

BackgroundWhile the association of chronological age with DNA methylation (DNAm) in whole blood has been extensively studied, the tissue-specificity of age-related DNAm changes remains an active area of research. Studies investigating the association of age with DNAm in tissues such as brain, skin, immune cells, fat, and liver have identified tissue-specific and non-specific effects, thus, motivating additional studies of diverse human tissue and cell types.ResultsHere, we performed an epigenome-wide association study, leveraging DNAm data (Illumina EPIC array) from 961 tissue samples representing 9 tissue types (breast, lung, colon, ovary, prostate, skeletal muscle, testis, whole blood, and kidney) from the Genotype-Tissue Expression (GTEx) project. We identified age-associated CpG sites (false discovery rate < 0.05) in 8 tissues (all except skeletal muscle, n = 47). This included 162,002 unique hypermethylated and 90,626 hypomethylated CpG sites across all tissue types, with 130,137 (80%) hypermethylated CpGs and 74,703 (82%) hypomethylated CpG sites observed in a single tissue type. While the majority of age-associated CpG sites appeared tissue-specific, the patterns of enrichment among genomic features, such as chromatin states and CpG islands, were similar across most tissues, suggesting common mechanisms underlying cellular aging. Consistent with previous findings, we observed that hypermethylated CpG sites are enriched in regions with repressed polycomb signatures and CpG islands, while hypomethylated CpG sites preferentially occurred in non-CpG islands and enhancers. To gain insights into the functional effects of age-related DNAm changes, we assessed the correlation between DNAm and local gene expression changes to identify age-related expression quantitative trait methylation (age-eQTMs). We identified several age-eQTMs present in multiple tissue-types, including in the CDKN2A, HENMT1, and VCWE regions.ConclusionOverall, our findings will aid future efforts to develop biomarkers of aging and understand mechanisms of aging in diverse human tissue types.

Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration.

Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.

Development of a Global Metabo-Lipid-Prote-omics Workflow to Compare Healthy Proximal and Distal Colonic Epithelium in Mice.

A multimetabo-lipid-prote-omics workflow was developed to characterize the molecular interplay within proximal (PC) and distal (DC) colonic epithelium of healthy mice. This multiomics data set lays the foundation to better understand the two tissue types and can be used to study, for example, colon-related diseases like colorectal cancer or inflammatory bowel disease. First, the methyl tert-butyl ether extraction method was optimized, so that from a single tissue biopsy >350 reference-matched metabolites, >1850 reference-matched lipids, and >4500 proteins were detected by using targeted and untargeted metabolomics, untargeted lipidomics, and proteomics. Next, each omics-data set was analyzed individually and then merged with the additional omics disciplines to generate a deep understanding of the underlying complex regulatory network within the colon. Our data demonstrates, for example, differences in mucin formation, detected on substrate level as well as on enzyme level, and altered lipid metabolism by the detection of phospholipases hydrolyzing sphingomyelins to ceramides. In conclusion, the combination of the three mass spectrometry-based omics techniques can better entangle the functional and regional differences between PC and DC tissue compared to each single omics technique.

Collagen patches releasing phosphatidylserine liposomes guide M1-to-M2 macrophage polarization and accelerate simultaneous bone and muscle healing

Bilateral communication between bones and muscles is essential for healing composite bone–muscle injuries from orthopedic surgeries and trauma. However, these injuries are often characterized by exaggerated inflammation, which can disrupt bone–muscle crosstalk, thereby seriously delaying the healing of either tissue. Existing approaches are largely effective at healing single tissues. However, simultaneous healing of multiple tissues remains challenging, with little research conducted to date. Here we introduce collagen patches that overcome this overlooked issue by harnessing the plasticity of macrophage phenotypes. Phosphatidylserine liposomes (PSLs) capable of shifting the macrophage phenotype from inflammatory M1 into anti-inflammatory/prohealing M2 were coated on collagen patches via a layer-by-layer method. Original collagen patches failed to improve tissue healing under inflammatory conditions coordinated by M1 macrophages. In contrast, PSL-coated collagen patches succeeded in accelerating bone and muscle healing by inducing a microenvironment dominated by M2 macrophages. In cell experiments, differentiation of preosteoblasts and myoblasts was completely inhibited by secretions of M1 macrophages but unaffected by those of M2 macrophages. RNA-seq analysis revealed that type I interferon and interleukin-6 signaling pathways were commonly upregulated in preosteoblasts and myoblasts upon stimulation with M1 macrophage secretions, thereby compromising their differentiation. This study demonstrates the benefit of PSL-mediated M1-to-M2 macrophage polarization for simultaneous bone and muscle healing, offering a potential strategy toward simultaneous regeneration of multiple tissues. Statement of significanceExisting approaches for tissue regeneration, which primarily utilize growth factors, have been largely effective at healing single tissues. However, simultaneous healing of multiple tissues remains challenging and has been little studied. Here we demonstrate that collagen patches releasing phosphatidylserine liposomes (PSLs) promote M1-to-M2 macrophage polarization and are effective for simultaneous healing of bone and muscle. Transcriptome analysis using next-generation sequencing reveals that differentiation of preosteoblasts and myoblasts is inhibited by the secretions of M1 macrophages but promoted by those of M2 macrophages, highlighting the importance of timely regulation of M1-to-M2 polarization in tissue regeneration. These findings provide new insight to tissue healing of multiple tissues.

The circadian rhythm: A key variable in aging?

The determination of age-related transcriptional changes may contribute to the understanding of health and life expectancy. The broad application of results from age cohorts may have limitations. Altering sample sizes per time point or sex, using a single mouse strain or tissue, a limited number of replicates, or omitting the middle of life can bias the surveys. To achieve higher general validity and to identify less distinctive players, bulk RNA sequencing of a mouse cohort, including seven organs of two strains from both sexes of 5 ages, was performed. Machine learning by bootstrapped variable importance and selection methodology (Boruta) was used to identify common aging features where the circadian rhythms (CiR) transcripts appear as promising age markers in an unsupervised analysis. Pathways of 11 numerically analyzed local network clusters were affected and classified into four major gene expression profiles, whereby CiR and proteostasis candidates were particularly conspicuous with partially opposing changes. In a data-based interaction association network, the CiR-proteostasis axis occupies an exposed central position, highlighting its relevance. The computation of 11,830 individual transcript associations provides potential superordinate contributors, such as hormones, to age-related changes, as in CiR. In hormone-sensitive LNCaP cells, short-term supraphysiologic levels of the sex hormones dihydrotestosterone or estradiol increase the expression of the CiR transcript Bhlhe40 and the associated senescence regulator Cdkn2b (p15). According to these findings, the bilateral dysregulation of CiR appears as a fundamental protagonist of aging, whose transcripts could serve as a biological marker and its restoration as a therapeutic opportunity.

Transcriptomics and epigenetic data integration learning module on Google Cloud.

Abstract Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. Highlights

Spatial proteomics of human diabetic kidney disease, from health to class III.

Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD. Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1). These analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45+ inflammatory cells, basement membrane and the interstitium. DKD progression was associated with co-localised increases in inflammatory cells and collagen IV deposition, with concomitant loss of native proteins of each nephron segment. Cell-type frequency and neighbourhood analyses highlighted a significant increase in inflammatory cells and their adjacency to tubular and αSMA+ (α-smooth muscle actin-positive) cells in DKD. Finally, DKD progression showed marked regional variability within single tissue sections, as well as inter-individual variability within each DKD class. Using the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.

Multi-modal mass spectrometry imaging of a single tissue section.

Mass spectrometry imaging (MSI) was developed to visualize spatial chemical information within tissues, thereby facilitating spatial multi-omic analysis. However, due to the limited spatial information provided by individual modal MSI, correlating various chemical data within tissues remains a significant challenge. In recent years, multimodal MSI has garnered considerable attention due to its ability to visualize the spatial distributions of multiple biomolecules within tissues. Among the strategies employed in this field, multimodal imaging on a single tissue section circumvents multiple issues introduced by integration of images of consecutive tissue sections. In this minireview, we provide an overview of multimodal MSI on a single tissue section, with a particular focus on the use of Matrix-Assisted Laser Desorption/Ionization-MSI for spatial multi-omic investigations that offer a comprehensive and in-depth elucidation of the biological state and activities, aiming to inspire the development of new approaches in this field.

A chromosome-level reference genome assembly and a full-length transcriptome assembly of the giant freshwater prawn (Macrobrachium rosenbergii).

The giant freshwater prawn (Macrobrachium rosenbergii) is a key species in the aquaculture industry in several Asian, African, and South American countries. Despite a considerable growth in its production worldwide, the genetic complexities of M. rosenbergii various morphotypes pose challenges in cultivation. This study reports the first chromosome-scale reference genome and a high-quality full-length transcriptome assembly for M. rosenbergii. We employed the PacBio High Fidelity (HiFi) sequencing to obtain an initial draft assembly and further scaffolded it with the chromatin contact mapping (Hi-C) technique to achieve a final assembly of 3.73-Gb with an N50 scaffold length of 33.6 Mb. Repetitive elements constituted nearly 60% of the genome assembly, with simple sequence repeats and retrotransposons being the most abundant. The availability of both the chromosome-scale assembly and the full-length transcriptome assembly enabled us to thoroughly probe alternative splicing events in M. rosenbergii. Among the 2,041 events investigated, exon skipping represented the most prevalent class, followed by intron retention. Interestingly, specific isoforms were observed across multiple tissues. Additionally, within a single tissue type, transcripts could undergo alternative splicing, yielding multiple isoforms. We believe that the availability of a chromosome-level reference genome for M. rosenbergii, along with its full-length transcriptome, will be instrumental in advancing our understanding of the giant freshwater prawn biology and enhancing its molecular breeding programs, paving the way for the development of M. rosenbergii with valuable traits in commercial aquaculture.

IDO+ Endothelial Cells in Glomeruli of Kidney Transplantation Patients With Glomerulitis.

Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. However, long-term graft survival remains a challenge. The enzyme indoleamine 2,3 dioxygenase (IDO) has been reported to have immunomodulatory effects with IDO transcripts being elevated in both antibody-mediated rejection and T cell-mediated rejection. A metal-conjugated antibody panel for the staining of kidney biopsies was developed, allowing the visualization of 41 structural and immune markers on a single tissue slide to gain in-depth insight into the composition and localization of the immune cell compartment. Staining was applied to week 4 and 24 protocol biopsies of 49 patients as well as on 15 indication biopsies of the TRITON study and 4 additional transplantation biopsies with glomerulitis. A highly distinctive and specific glomerular IDO expression was observed in biopsies from 3 of 49 patients in imaging mass cytometry. Immunohistochemistry confirmed IDO expression in glomeruli of 10 of 10 cases with glomerulitis. IDO was found to be expressed by CD31+ glomerular endothelial cells, accompanied by the presence of granzyme-B+Tbet+CD7+CD45RA+ natural killer cells and CD68+ macrophages. Furthermore, a proportion of both the immune cells and endothelial cells expressed Ki-67, indicative of cell proliferation, which was not observed in control glomeruli. Our results show glomerular IDO expression in transplanted kidneys with glomerulitis, which is accompanied by increased numbers of natural killer cells and macrophages and likely reflects local immune activation.

Non-invasive and noise-robust light focusing using confocal wavefront shaping

Wavefront-shaping is a promising approach for imaging fluorescent targets deep inside scattering tissue despite strong aberrations. It enables focusing an incoming illumination into a single spot inside tissue, as well as correcting the outgoing light scattered from the tissue. Previously, wavefront shaping modulations have been successively estimated using feedback from strong fluorescent beads, which have been manually added to a sample. However, such algorithms do not generalize to neurons whose emission is orders of magnitude weaker. We suggest a wavefront shaping approach that works with a confocal modulation of both the illumination and imaging arms. Since the aberrations are corrected in the optics before the detector, the low photon budget is directed into a single sensor spot and detected with high signal-noise ratio. We derive a score function for modulation evaluation from mathematical principles, and successfully use it to image fluorescence neurons, despite scattering through thick tissue.

Comparative Evaluation of USG-Guided Single Tissue Marker Versus Multiple Tissue Marker Placements in Breast Malignancy Patients Undergoing Neoadjuvant Chemotherapy for Tumor Localization.

Background Breast cancer remains one of the most common malignancies affecting women globally, contributing significantly to the disease burden. The advent of neoadjuvant chemotherapy (NAC) has revolutionized the treatment for locally advanced breast cancer, allowing tumors to be downstaged and making breast-conserving surgery (BCS) feasible. Accurate localization of the tumor bed post-NAC is crucial for successful surgical removal of residual disease. While traditional single tissue marker placement has been effective, recent advances suggest multiple markers might provide superior localization by comprehensively delineating the entire tumor area. This study aims to compare the effectiveness of single versus multiple tissue marker placements in breast malignancy patients undergoing NAC. Materials and methods A prospective study was conducted in the Department of Radio-diagnosis at Saveetha Medical College over 18 months, including 10 patients diagnosed with breast carcinoma, selected through convenience sampling. Inclusion criteria involved patients diagnosed with breast cancer via mammography, sonography, and histological confirmation, referred for clip placement before NAC. Exclusion criteria were patients unwilling to participate. The procedure involved placing one to two surgical clips within the tumor using a 14/16-gauge coaxial guiding needle under USGguidance, with additional clips for larger or multiple tumors. Data collection included pre-procedural USG, post-procedural mammography (MG1), pre-operative mammography (MG2)/USG, and gross specimen histopathological examination/specimen mammography. Statistical analysis Demographic data, clipping distribution, receptor status, localization methods, surgical outcomes, operation diagnoses, and correlation analysis were statistically analyzed. Mean age, standard deviation, and p-values were calculated to determine the significance of differences between single and multiple clip groups. Results The study included 10 patients with a mean age of 52.5 years. Of these, five (50%)had a single clip, and two (20%)had four clips. The average time from clipping to the second mammogram (MG2) was 106.3 days, and from clipping to operation was 111.0 days, with longer follow-up times for multiple clip patients. Six (60%) of the patients were estrogen receptor (ER) positive, and six (60%) were human epidermal growth factor receptor 2 (HER2) negative. Localization methods were similar between single and multiple clip groups. However, multiple clip patients tended to undergo more extensive surgeries like modified radical mastectomy (MRM). Imaging responses showed no preoperative ultrasound lesions in single clip patients, while multiple clip patients had higher inconsistent diagnoses (10 (100%)) suggesting that multiple clips provide better tumor localization but are linked to increased complexity and longer follow-up times. Conclusion Patients with multiple clips experienced significantly longer follow-up times, reflecting more complex clinical scenarios. Despite no significant differences in receptor status distributions, multiple clip patients required more extensive surgeries, emphasizing the need for tailored surgical planning. The study underscores the importance of considering the number of clips in clinical decision-making. Future research should focus on larger, prospective studies to validate these findings and explore underlying mechanisms.

Surveying the landscape of RNA isoform diversity and expression across 9 GTEx tissues using long-read sequencing data.

Even though alternative RNA splicing was discovered nearly 50 years ago (1977), we still understand very little about most isoforms arising from a single gene, including in which tissues they are expressed and if their functions differ. Human gene annotations suggest remarkable transcriptional complexity, with approximately 252,798 distinct RNA isoform annotations from 62,710 gene bodies (Ensembl v109; 2023), emphasizing the need to understand their biological effects. For example, 256 gene bodies have ≥50 annotated isoforms and 30 have ≥100, where one protein-coding gene (MAPK10) even has 192 distinct RNA isoform annotations. Whether such isoform diversity results from biological redundancy or spurious alternative splicing (i.e., noise), or whether individual isoforms have specialized functions (even if subtle) remains a mystery for most genes. Recent studies by Aguzzoli-Heberle et al., Leung et al., and Glinos et al. demonstrated long-read RNAseq enables improved RNA isoform quantification for essentially any tissue, cell type, or biological condition (e.g., disease, development, aging, etc.), making it possible to better assess individual isoform expression and function. While each study provided important discoveries related to RNA isoform diversity, deeper exploration is needed. We sought to quantify and characterize real isoform usage across tissues (compared to annotations). We used long-read RNAseq data from 58 GTEx samples across nine tissues (three brain, two heart, muscle, lung, liver, and cultured fibroblasts) generated by Glinos et al. and found considerable isoform diversity within and across tissues. Cerebellar hemisphere was the most transcriptionally complex tissue (22,522 distinct isoforms; 3,726 unique); liver was least diverse (12,435 distinct isoforms; 1,039 unique). We highlight gene clusters exhibiting high tissue-specific isoform diversity per tissue (e.g., TPM1 expresses 19 in heart's atrial appendage). We also validated 447 of the 700 new isoforms discovered by Aguzzoli-Heberle et al. and found that 88 were expressed in all nine tissues, while 58 were specific to a single tissue. This study represents a broad survey of the RNA isoform landscape, demonstrating isoform diversity across nine tissues and emphasizes the need to better understand how individual isoforms from a single gene body contribute to human health and disease.

Improved resilience and proteostasis mediate longevity upon DAF-2 degradation in old age

Little is known about the possibility of reversing age-related biological changes when they have already occurred. To explore this, we have characterized the effects of reducing insulin/IGF-1 signaling (IIS) during old age. Reduction of IIS throughout life slows age-related decline in diverse species, most strikingly in the nematode Caenorhabditis elegans. Here we show that even at advanced ages, auxin-induced degradation of DAF-2 in single tissues, including neurons and the intestine, is still able to markedly increase C. elegans lifespan. We describe how reversibility varies among senescent changes. While senescent pathologies that develop in mid-life were not reversed, there was a rejuvenation of the proteostasis network, manifesting as a restoration of the capacity to eliminate otherwise intractable protein aggregates that accumulate with age. Moreover, resistance to several stressors was restored. These results support several new conclusions. (1) Loss of resilience is not solely a consequence of pathologies that develop in earlier life. (2) Restoration of proteostasis and resilience by inhibiting IIS is a plausible cause of the increase in lifespan. And (3), most interestingly, some aspects of the age-related transition from resilience to frailty can be reversed to a certain extent. This raises the possibility that the effect of IIS and related pathways on resilience and frailty during aging in higher animals might possess some degree of reversibility.

Differences in Toxicokinetics and Maternal Transfer between Lipophilic and Proteinophilic Halogenated Organic Pollutants in Laying Hens.

In this study, we conducted exposure experiments on egg-laying hens to explore the toxicokinetics and maternal transfer characteristics of lipophilic and proteinophilic halogenated organic pollutants (HOPs). The lipophilic HOPs included polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and dechlorane plus (DPs), while the proteinophilic HOPs included perfluorocarboxylic acids (PFCAs). The results revealed that most of lipophilic HOPs exhibit lower depuration rate (k d) than PFCAs. The k d of lipophilic HOPs correlated with the octanol-water partition coefficient (log K OW) values in a V-shaped curve, whereas that of PFCAs correlated with the protein-water partition coefficient (log K PW) values in an inverted V-shaped curve. The depuration rate, rather than the uptake rate, was a leading factor in determining the bioaccumulation potential of HOPs in hens. Although the dominant factors determining the tissue distribution of the two types of compounds were explicit (fats vs phospholipids), chemical-specific tissue distribution was still observed. The egg-maternal concentration ratio was dependent on the exposure status, concentration, and maternal tissue choice. Using a single maternal tissue may not be an appropriate method for assessing chemical maternal transfer potential. PFCAs have a greater maternal transfer potential (>80% of the total body burden) than lipophilic HOPs (approximately 30% for BDE209 and DPs, and less than 10% for the others). Their lipophilic and partly proteinophilic nature makes the toxicokinetics and maternal transfer characteristics of BDE209 and DPs different from those of other lipophilic HOPs. These findings are crucial for enhancing our understanding of the behavior and fate of HOPs in egg-laying hens.

Receptors Implicated in Microgravity-Induced Bone Loss

For humans to explore and colonize the universe, both engineering and physiological obstacles must be successfully addressed. A major physiological problem is that humans lose bone rapidly in microgravity. Understanding the underlying mechanisms for this bone loss is crucial for designing strategies to ameliorate these effects. Because bone physiology is entangled with other organ systems, and bone loss is a component of human adaptation to microgravity, strategies to reduce bone loss must also account for potential effects on other systems. Here, we consider the receptors involved in normal bone remodeling and how this regulation is altered in low-gravity environments. We examine how single cells, tissues and organs, and humans as a whole are affected by low gravity, and the role of receptors that have been implicated in responses leading to bone loss. These include receptors linking cells to the extracellular matrix and to each other, alterations in the extracellular matrix associated with changes in gravity, and changes in fluid distribution and fluid behavior due to lack of gravity that may have effects on receptor-based signaling shared by bone and other regulatory systems. Inflammatory responses associated with the environment in space, which include microgravity and radiation, can also potentially trigger bone loss.