Spatial and single-cell transcriptomics reveal the reorganization of cerebellar microglia with aging.

The emergence of tools enabling researchers to perform high-plex spatial transcriptomics with single-cell resolution has revolutionized our understanding of tumor development. However, tissue samples with degraded RNA or extensive crosslinking present challenges for gene expression measurements. For decades, cancer tissue samples have been collected and preserved using formalin-fixation and embedding in paraffin (FFPE), which is not optimal for preserving RNA integrity. This has limited the depth of insights researchers can obtain from archival samples. The Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) technology facilitates direct RNA profiling in situ with high sensitivity and resolution. The high accuracy and high plexity are enabled by tiling probes along the length of a transcript, which is particularly challenging in samples with fragmented RNA, such as in FFPE. The MERFISH 2.0 chemistry and sample preparation workflow was developed specifically to improve transcript detection efficiency in measurement of up to 1000 genes in low quality tissues. These improvements can be applied to tissues of up to 3cm2 using the MERSCOPE® Ultra™ Platform. Here we applied the improved capabilities of MERFISH 2.0 to profile human breast cancer and human lung cancer tumors over a range of sample qualities. Breast and lung tumor samples, both whole sections and tissue microarrays (TMA), were run on the MERSCOPE Ultra Platform using a panel of 815 immuno-oncology genes with both MERFISH 1.0 and MERFISH 2.0. The resulting data was used for spatial and single-cell analyses. We show that in all tissues, particularly in lower quality tissues, MERFISH 2.0 increases the counts and subsequent quality of data analysis, such as increased detection of cell types and increased neighborhood associations. Inter- and intra-tumor immune profiling showed improved detection of low-expressing markers, enabling deeper study of the tumor microenvironment. Spatially resolved transcriptomic profiling of low-quality samples at single-cell level offers significant opportunities for understanding how cancers develop in situ. These improvements will enable new genomic inquiries into how tumors develop and modulate their environment, which in turn will open up new areas of therapeutic research. Citation Format: Manisha Ray, Justin He, Bin Wang, Bing Yang, Sudhir Tattikota, Timothy Wiggin, Hao Wang, Jichuan Zhang, Lizi Maziashvili, Alexander Genshaft, Peter Reinhold, Brittany Auclair, Robert Mathis, Shawn Wang, Jiang He, George Emanuel. New developments in spatial transcriptomics unlock insights into the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5307.

Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.

Characterization of Niche Cells and Signals Regulating Stepwise Embryonic Hematopoiesis Using Slide-Seq and Merfish Spatial Transcriptomics

SummaryHybridization-based spatial transcriptomics technologies have advanced our ability to map cellular and subcellular organization in complex tissues. However, existing methods remain constrained in gene coverage, multimodal compatibility, and scalability. Here, we present MERFISH+, an enhanced version of Multiplexed Error-Robust Fluorescence in Situ Hybridization (MERFISH), which integrates chemical probe anchoring in protective hydrogels with high-throughput microfluidics and microscopy. This optimized design supports robust and repeated hybridization cycles across an entire centimeter-scale tissue sample. MERFISH+ allowed to simultaneously quantify over 1,800 genes and resolve the 3D organization of chromatin loci and their associated epigenomic marks in developing human hearts. Using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omic atlas of the developing human heart at subcellular resolution capturing 3.1 million cells across 34 distinct populations. This 3D atlas provides a holistic view of an entire organ enabling the characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Thus, MERFISH+ offers a robust, large-format platform for spatial multi-omics that enables high resolution mapping of gene expression at subcellular resolution and the characterization of cellular organization within 3D organs.

Spatial transcriptomics has emerged as a powerful tool to define the cellular structure of diverse tissues. One such method is multiplexed error robust fluorescence in situ hybridization (MERFISH). MERFISH identifies RNAs with error tolerant optical barcodes generated through sequential rounds of single-molecule fluorescence in situ hybridization (smFISH). MERFISH performance depends on a variety of protocol choices, yet their effect on performance has yet to be systematically examined. Here we explore a variety of properties to identify optimal choices for probe design, hybridization, buffer storage, and buffer composition. In each case, we introduce protocol modifications that can improve performance, and we show that, collectively, these modified protocols can improve MERFISH quality in both cell culture and tissue samples. As RNA FISH-based methods are used in many different contexts, we anticipate that the optimization experiments we present here may provide empirical design guidance for a broad range of methods.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-17477-1.

BACKGROUND Recent studies using single-cell RNA sequencing (scRNA-seq) have provided insights into the cellular composition and molecular states in glioblastoma (GBM). However, these studies have not identified significant differences between primary and recurrent GBMs. We hypothesize that other factors, such as spatial organization and cellular interactions, may provide key information driving recurrence in GBM. Spatial transcriptomics techniques, such as MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), provide single-cell spatial resolution of tissues, enabling the identification of distinct molecular features. MERFISH, in particular, allows for the 3D reconstruction of gene expression signatures in consecutive tissue slices, providing a detailed spatial map of the tumor cells and surrounding environment. This technology is invaluable in studying longitudinal GBMs, where changes in tumor organization, immune cell interactions, and mechanisms of therapeutic resistance remain poorly understood. MATERIAL AND METHODS We analyzed 102 tumor samples consisting of primary and recurrent GBMs and normal brain tissue using Visium and MERSCOPE technologies. We used supervised artificial intelligence (AI)-based learning, in-silico perturbation and explainable-AI to identify distinct spatial and cell-type-specific transcriptomic reorganization associated with GBM recurrence. RESULTS We developed a spatial transcriptomics atlas of GBM at sub-cellular resolution. Our analysis revealed distinct cellular neighborhoods in the primary and recurrent GBM tumors, with changes leading to the dynamic spatial reorganization of GBM at recurrence. The 3D spatial images acquired provided a comprehensive view of the tumor cells interacting with the tumor microenvironment in situ. Our explainable-AI models successfully identified distinct spatial transcriptomic features associated with recurrence in GBM. The identified spatially distinct cellular neighborhoods reveal dynamic shift in cellular organization following in silico perturbation. CONCLUSION This study provides a high-resolution spatial map of longitudinal GBM that offers new insights into the dynamic cellular reorganization of GBM at recurrence. Our findings highlight the role of cellular reorganization during tumor recurrence in GBM, a potential avenue for targeted therapeutic intervention.

Investigation of frailty as a moderator of the relationship between neuropathology and dementia in Alzheimer's disease: a cross-sectional analysis of data from the Rush Memory and Aging Project

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

The expression profiles and spatial distributions of RNAs regulate many cellular functions. Image-based transcriptomic approaches provide powerful means to measure both expression and spatial information of RNAs in individual cells within their native environment. Among these approaches, multiplexed error-robust fluorescence in situ hybridization (MERFISH) has achieved spatially resolved RNA quantification at transcriptome scale by massively multiplexing single-molecule FISH measurements. Here, we increased the gene throughput of MERFISH and demonstrated simultaneous measurements of RNA transcripts from ∼10,000 genes in individual cells with ∼80% detection efficiency and ∼4% misidentification rate. We combined MERFISH with cellular structure imaging to determine subcellular compartmentalization of RNAs. We validated this approach by showing enrichment of secretome transcripts at the endoplasmic reticulum, and further revealed enrichment of long noncoding RNAs, RNAs with retained introns, and a subgroup of protein-coding mRNAs in the cell nucleus. Leveraging spatially resolved RNA profiling, we developed an approach to determine RNA velocity in situ using the balance of nuclear versus cytoplasmic RNA counts. We applied this approach to infer pseudotime ordering of cells and identified cells at different cell-cycle states, revealing ∼1,600 genes with putative cell cycle-dependent expression and a gradual transcription profile change as cells progress through cell-cycle stages. Our analysis further revealed cell cycle-dependent and cell cycle-independent spatial heterogeneity of transcriptionally distinct cells. We envision that the ability to perform spatially resolved, genome-wide RNA profiling with high detection efficiency and accuracy by MERFISH could help address a wide array of questions ranging from the regulation of gene expression in cells to the development of cell fate and organization in tissues.

Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows simultaneous imaging of numerous RNA species in their native cellular environment and hence spatially resolved single-cell transcriptomic measurements. However, the relatively modest brightness of signals from single RNA molecules can become limiting in a number of applications, such as increasing the imaging throughput, imaging shorter RNAs, and imaging samples with high degrees of background, such as some tissue samples. Here, we report a branched DNA (bDNA) amplification approach for MERFISH measurements. This approach produces a drastic signal increase in RNA FISH samples without increasing the fluorescent spot size for individual RNAs or increasing the variation in brightness from spot to spot, properties that are important for MERFISH imaging. Using this amplification approach in combination with MERFISH, we demonstrated RNA imaging and profiling with a near 100% detection efficiency. We further demonstrated that signal amplification improves MERFISH performance when fewer FISH probes are used for each RNA species, which should allow shorter RNAs to be imaged. We anticipate that the combination of bDNA amplification with MERFISH should facilitate many other applications and extend the range of biological questions that can be addressed by this technique in both cell culture and tissues.

Spatial transcriptomics is an emerging technology that maps gene expression within tissue architecture. Its expanding use in medicine and veterinary science supports research, precision diagnostics, biomarker discovery, and development of targeted treatment strategies. While spatial transcriptomics applications in human health are well-documented with significant publication diversity and volume, published applications in veterinary medicine remain limited. A comprehensive search of PubMed was conducted, focusing on studies published from 2016 to early 2025 that employed spatial transcriptomics in the context of disease research, diagnosis, or treatment in human or animal health. The review followed the Arksey and O’Malley framework and adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 1398 studies met the inclusion criteria. The studies highlighted emerging trends of comparative research with animal model use for human health research. Commonly used spatial transcriptomics platforms included 10× Visium, Slide-seq, Nanostring (GeoMx, CosMX), and multiplexed error-robust fluorescence in situ hybridization (MERFISH). Key gaps in publications include limited veterinary representation, interspecies comparisons, standardized methods, public data use, and therapeutic studies, alongside biases in disease, species, organ, and geography. This review presents the current landscape of spatial transcriptomics publications for human and animal research and medicine, providing comprehensive data and highlighting underrepresented research areas and gaps for future consideration.

Long non-coding RNAs (lncRNAs) are critical regulators of physiological and pathological processes, with their dysregulation increasingly implicated in aging and Alzheimer's disease (AD). To investigate the spatial and cellular distribution of lncRNAs in the aging brain, we leveraged published spatial transcriptomics (ST), single-nucleus RNA sequencing (snRNA-seq), and bulk RNA-seq datasets from the dorsolateral prefrontal cortex (DLPFC) of ROSMAP participants with and without pathological AD. LncRNAs exhibited greater subregion-specific expression than mRNAs, with enrichment in antisense and lincRNA biotypes. Subregion-enriched lncRNAs were generally not cell-type specific, and vice versa. Differential expression analysis of ST data identified AD-associated lncRNAs with distinct spatial patterns and moderate overlap with differentially expressed (DE) lncRNAs from bulk RNA-seq. Gene set enrichment revealed their involvement in chromatin remodeling, epigenetic regulation, and RNA metabolism. We also identified AD DE lncRNAs across major brain cell types using snRNA-seq but overlap with ST DE lncRNAs was limited. Among previously reported lncRNAs, OIP5-AS1 was consistently upregulated in AD in all cortical subregions. Antisense oligonucleotide (ASO) knockdown of OIP5-AS1 in iPSC-derived microglia led to upregulation of pro-inflammatory genes and downregulation of DNA replication and repair pathways. Immunoassays confirmed increased secretion of pro-inflammatory cytokines. The knockdown expression pattern was enriched for microglia-specific AD DE genes and microglia states. This study provides a spatial and cellular map of lncRNAs in the aging human cortex and identifies subregion-and cell-type-enriched DE lncRNAs in AD. Our findings implicate OIP5-AS1 in microglial activation, suggesting its potential contribution to AD pathogenesis.

Hyperphosphorylated transactive response DNA-binding protein 43 (TDP-43, encoded by TARDBP ) proteinopathy has recently been described in ageing and in association with cognitive impairment, especially in the context of Alzheimer's disease pathology. To explore the role of mixed Alzheimer's disease and TDP-43 pathologies in clinical Alzheimer's-type dementia, we performed a comprehensive investigation of TDP-43, mixed pathologies, and clinical Alzheimer's-type dementia in a large cohort of community-dwelling older subjects. We tested the hypotheses that TDP-43 with Alzheimer's disease pathology is a common mixed pathology; is related to increased likelihood of expressing clinical Alzheimer's-type dementia; and that TDP-43 pathologic stage is an important determinant of clinical Alzheimer's-type dementia. Data came from 946 older adults with ( n = 398) and without dementia ( n = 548) from the Rush Memory and Aging Project and Religious Orders Study. TDP-43 proteinopathy (cytoplasmic inclusions) was present in 496 (52%) subjects, and the pattern of deposition was classified as stage 0 (none; 48%), stage 1 (amygdala; 18%), stage 2 (extension to hippocampus/entorhinal; 21%), or stage 3 (extension to neocortex; 14%). TDP-43 pathology combined with a pathologic diagnosis of Alzheimer's disease was a common mixed pathology (37% of all participants), and the proportion of subjects with clinical Alzheimer's-type dementia formerly labelled 'pure pathologic diagnosis of Alzheimer's disease' was halved when TDP-43 was considered. In logistic regression models adjusted for age, sex, and education, TDP-43 pathology was associated with clinical Alzheimer's-type dementia (odds ratio = 1.51, 95% confidence interval = 1.11, 2.05) independent of pathological Alzheimer's disease (odds ratio = 4.30, 95% confidence interval = 3.08, 6.01) or other pathologies (infarcts, arteriolosclerosis, Lewy bodies, and hippocampal sclerosis). Mixed Alzheimer's disease and TDP-43 pathologies were associated with higher odds of clinical Alzheimer's-type dementia (odds ratio = 6.73, 95% confidence interval = 4.18, 10.85) than pathologic Alzheimer's disease alone (odds ratio = 4.62, 95% confidence interval = 2.84, 7.52). In models examining TDP-43 stage, a dose-response relationship with clinical Alzheimer's-type dementia was observed, and a significant association was observed starting at stage 2, extension beyond the amygdala. In this large sample from almost 1000 community participants, we observed that TDP-43 proteinopathy was very common, frequently mixed with pathological Alzheimer's disease, and associated with a higher likelihood of the clinical expression of clinical Alzheimer's-type dementia but only when extended beyond the amygdala.

As an image-based single-cell transcriptomics approach, multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows hundreds to thousands of RNA species to be identified, counted and localized in individual cells while preserving the native spatial context of RNAs. In MERFISH, RNAs are identified via a combinatorial labeling approach that encodes RNA species with error-robust barcodes followed by sequential rounds of single-molecule FISH (smFISH) to read out these barcodes. The accuracy of RNA identification relies on spatially separated signals from individual RNA molecules, which limits the density of RNAs that can be measured and makes the multiplexed imaging of a large number of high-abundance RNAs challenging. Here we report an approach that combines MERFISH and expansion microscopy to substantially increase the total density of RNAs that can be measured. Using this approach, we demonstrate accurate identification and counting of RNAs, with a near 100% detection efficiency, in a ~130-RNA library composed of many high-abundance RNAs, the total density of which is more than 10 fold higher than previously reported. In parallel, we demonstrate the combination of MERFISH with immunofluorescence in expanded samples. These advances increase the versatility of MERFISH and will facilitate its application to a wide range of biological problems.

Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains. We describe a segmentation method, Baysor, that optimizes two-dimensional (2D) or three-dimensional (3D) cell boundaries considering joint likelihood of transcriptional composition and cell morphology. While Baysor can take into account segmentation based on co-stains, it can also perform segmentation based on the detected transcripts alone. To evaluate performance, we extend multiplexed error-robust fluorescence in situ hybridization (MERFISH) to incorporate immunostaining of cell boundaries. Using this and other benchmarks, we show that Baysor segmentation can, in some cases, nearly double the number of cells compared to existing tools while reducing segmentation artifacts. We demonstrate that Baysor performs well on data acquired using five different protocols, making it a useful general tool for analysis of imaging-based spatial transcriptomics.