Sequencing-based Spatial Transcriptomics with High Sensitivity

Sequencing-based Spatial Transcriptomics with High Sensitivity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sequencing-based Spatial Transcriptomics with High Sensitivity Gufeng Wang, Renjie Liao, Defeng Fu, Zaoxu Xu, Han Liang, Xiaoran Zhou, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7337510/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The advent of spatial transcriptomics has dramatically expanded our ability to study the vast network of cell-cell interactions at the molecular level in tissue. Among current methods, sequencing-based approaches have great potential in discovery because of their unbiased capture of the whole transcriptome. In the last couple of years, the spatial resolution for the capture addresses has been improved from ~100 μm to <1 μm, well below the size of a mammalian cell. However, the capture efficiency of sequencing-based methods is generally low, which diminishes the effect of high capture resolution and negatively impacts subsequent data interpretation. Here, we introduce a new spatial transcriptomic system, which provides ~1 μm capture resolution and a much higher capture efficiency. We demonstrate that as high as ~14,000 UMIs can be captured from mouse testis sample per 10 × 10 μm² area at a sequencing saturation of 0.43. Rare cells organized in single-cell-layers can be recovered when the data was analyzed at an effective resolution of 2 μm. Biological sciences/Molecular biology/Transcriptomics Biological sciences/Biological techniques/Gene expression analysis/Microarray analysis Spatial transcriptomics sequencing-based spatial resolution spermatogenesis hippocampus cortex Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction It has been long ever since researchers started to explore gene expression using RNA transcripts at cell- and tissue-levels 1 . Initial efforts included those hybridization probe-based imaging methods, like fluorescence in situ hybridization (FISH) 2 . Not until very recently can this approach be called spatial transcript-OMICS (e.g., MERFISH 3 , seqFISH 4 and STARmap 5 ) when a large panel of genes (over several hundred) can be studied in one experiment. However, these methods rely on preliminary knowledge and well-designed panels, limiting their roles in discovery. Sequencing-based spatial transcriptomics methods, on the other hand, capture the whole genome expression profile using spatially barcoded probes. For this reason, these techniques are also referred to as “unbiased methods”. A group of techniques emerged, such as Visium 6 , Slide-seq 7,8 , HDST 9 , DBIT 10 , Decoder-Seq 11 , Seq-Scope 12 , Stereo-seq 13 , Open-ST 14 , Nova-ST 15 , PixelSeq 16 , Visium HD 17 , which drastically expedited the advance of relevant fields. So far, they have shown their ability to determine cell-type architecture of tissue, reveal cell–cell interactions, and monitor cell differentiation and tissue development, etc. 18–20 Some of these methods’ spatial resolution for capture addresses is improved to be 0.5~2 μm 21 , sufficiently high to support cell-level studies. However, current sequencing-based methods exhibit limited capture efficiency of mRNA molecules. While a mammalian cell typically contains 10 5 –10 6 mRNA molecules, and expresses up to 10,000 different genes 22 , spatial transcriptomics experiments usually report the detection of several hundred mRNAs per 10 × 10 μm² area on a capture chip, a size similar to that of a cell, at a reasonable sequencing saturation. For examples, Chen et al. utilized Slide-seq to investigate spermatogenesis and mechanisms of diabetes-induced male infertility, achieving a mean detection level of 784 UMIs per bead (10 μm diameter) 8 . Xu et al. explored mouse liver cell functions and molecular modulations during liver regeneration using Stereo-seq 23 , capturing a median of ~800 UMIs per 10 × 10 μm² area (Note different studies used differently binned areas for analysis. For the sake of comparison, all reported UMIs are normalized according to a unit area of 10 × 10 μm²). Similarly, Seq-Scope was employed to study the homeostatic liver, achieving a capture efficiency comparable to Stereo-seq (median ~800 UMIs per 10 × 10 μm² area) 12 . Visium (10× Genomics) has been widely used in various type of tissue, such as mouse pulmonary fibrosis (median ~30 UMIs per 10 × 10 μm² area) 24 , damage healing of mouse intestine (average ~120 UMIs per 10 × 10 μm² area) 25 , and mouse organogenesis 26 (median ~220 UMIs per 10 × 10 μm² area). The gene expression level varies hugely in different types of tissue. The reported mRNA detection in these applications is usually in the range of several tens to several hundred UMIs per 10 × 10 μm² area. As a comparison, single cell RNA sequencing (scRNA-seq) experiments typically report the capture of several thousand mRNA molecules in a cell 2728 29 , ~ one order of magnitude higher. Note that this difference in the amounts of captured mRNAs is NOT merely caused by different sequencing depths as both methods normally sequence the DNA library to a reasonable saturation level (0.5-0.7). An accurate gene expression profile is essential for subsequent bioinformatics analysis. The greater the number and variety of transcripts that can be detected, the more comprehensive and accurate the transcriptomic state of a tissue is. Low capture efficiency diminishes the effort of improving the capture address resolution because the data has to be “binned” to a poorer resolution so that there are sufficient UMIs in each unit and they can be analyzed. Furthermore, it has several profound negative impact on the data interpretation: (1) difficulty in cell type identification and classification; (2) missing rare cell types and sub-types; (3) missing low-abundance genes that may play a key role in biological processes; (4) inaccurate cell-cell interactions due to missing expressed genes; (5) limited resolving power for dynamic gene expression changes upon external perturbation; (6) susceptible to background noises such as non-specific binding, sequencing error, etc. In this manuscript, we present experimental setup and most recent data from a new spatial transcriptomic system (Salus-STS). It provides ~1 μm capture resolution and the surface capture probe density is raised to ~55,000/μm², which yields a capture efficiency approaching that of scRNA-seq techniques. We show that per 10 × 10 μm² area , we are able to capture ~14,000 UMIs from mouse testis sample with a saturation of 0.43, ~2,800 UMIs from adult mouse brain sample with a saturation of 0.46, and ~5,000 UMIs from mouse liver with a saturation of 0.70. To the best of our knowledge, these are the highest number of captures that has been reported for ST experiments in the literature. Finally, we demonstrate that such a high capture efficiency allows us to analyze the data at an effective resolution of 2 μm, which discloses fine tissue structures that are challenging to observe with other methods. Results Capture substrate design Consider an equilibrium surface binding reaction: S + A = SA , where S stands for the surface binding site, A stands for the target molecule being captured, SA stands for the target-surface site complex. In ST experiments, only a small portion of probes (less than 1%) bound to the mRNA targets, so the depletion of the surface binding site S is negligible. In this case, the surface concentration of captured targets can be approximated as [SA]=[A] 0 /(1+1/Kc) , where [ SA ] and [ A ] 0 stand for the surface concentration of reacted and initial target molecules, respectively, c is the initial concentration of surface binding site S in the solution, and K is the binding constant. Before Kc reaches a large enough value (0.1), [SA] ≈ [A] 0 * Kc , it is clear that the amount of the mRNAs captured is directly proportional to the initial surface concentration of the capture sites. Thus, the key to improve mRNA capture efficiency is to increase the amount of capture probes per unit area. Bearing this in mind, we designed Salus-STS capture substrate as depicted in Fig. 1. A next generation DNA sequencer Salus Pro (Salus BioMed) operating under the sequencing-by-synthesis (SBS) principle 30,31 served as the platform for generating ultra-high-density capture probes on substrate with random spatial barcodes (SBCs), and for decoding the SBCs. Note that there are recent efforts using Illumina sequencing flowcells to fabricate the capture substrates, e.g., Seq-Scope 12 , Open-ST 14 , Nova-ST 15 , etc. However, since they had to use the original Illumina sequencing flowcell surface, their capability in optimizing the experiments is largely hindered. Here, we designed the capture substrate so that it is compatible with a Salus Pro DNA sequencer. The capture areas are either 8.6×8.6 mm 2 or 11×11 mm 2 . The primers on the surface for bridge amplification were redesigned so they would not interfere with standard Illumina library construction. The stalk portion of the capture probes contained a random 30-bp nucleotide sequence serving as the spatial barcode, which holds excess complexity (over 10 18 ) for spatial mapping over an area larger than 1 cm 2 . In the fabrication of the capture substrate, the stalks of the capture probes were bridge amplified on the substrate with optimized primer surface density. Through multiple rounds of amplification, clusters of the stalks formed with a surface density of ~1 cluster/μm 2 , equivalent to a center-to-center distance of ~1 μm (Fig. 1A). In this setup, the stalks of the capture probes were randomly hybridized on the surface, leading to randomly distributed probes. Subsequently, the Salus Pro DNA sequencer was employed to decode random SBCs for each physical address on the substrate. Following decoding, the molecular recognition portions of the probes were added to the stalks with a guided extension approach (Fig. 1B). These recognition portions consist of unique molecular identifiers (UMIs) and polyT sequences. UMIs are random 10-bp nucleotides serving to distinguish transcripts of the same gene locus captured by the same cluster of probes. PolyT sequences were used to capture mRNAs in an unbiased manner through hybridization with mRNAs’ polyA tails. The capture surface prepared this way has a spatial capture resolution of ~1 μm (Fig. 1C); the capture probe density on the surface was maximized to be 55,000 +/- 10,000 /μm 2 (see Methods, Fig. S1), much higher than those current methods 6 (Fig. 1D). Capture saturation and efficiency Following the substrate fabrication step are the standard protocols for sequencing-based spatial transcriptomics, which can be completed in any biological research lab equipped with a cryo- microtome. The standard protocols include frozen tissue micro-sectioning, tissue slice being mounted on the substrate, fixation, permeabilization, and mRNA capturing by the probes. Subsequently, reverse transcription and second-strand synthesis are performed. The second strands are then denatured, washed off, and collected. The resulting cDNAs are utilized for library construction and subjected to paired-end sequencing in conjunction with SBCs. Through the alignment of each cDNA to the SBC map, a high-resolution spatial transcriptomic landscape is constructed. In ST experiments, the concept of sequencing saturation is introduced to help researchers evaluate whether the ST library has been sequenced to sufficient depth. Sequencing Saturation = 1 - (unique reliable alignment reads/all reliable alignment reads). Generally, when the number of sequencing reads is increased, one obtains more UMIs. However, the increasing rate of the UMIs slows down drastically as the sequencing saturation approaching 1. To give a clearer picture, we simulated the relationships between reads count, UMIs and saturations at different level of capture efficiency (see Methods). In the simulation, a total number of 5 million, 10 million, 20 million RNAs per mm 2 are captured, respectively (Fig. S2). Notably, if the capture efficiency of a ST experiment is low, the recovered UMIs and saturation level will level off at relatively low sequencing reads (Fig. S2A), which means further increasing the sequencing depth will NOT recover more UMIs. Since ST libraries are rarely sequenced to be above 0.9 saturation due to sequencing cost concerns, and actual sequencing depth varies in different studies, a fair comparison for different methods is to compare UMIs at the same sequencing saturation level (Fig. S2B, C). Clearly, the larger number of UMIs recovered, the higher capture efficiency it indicates. A proper range of saturation level of ST experiments will be 0.5 to 0.7, as it recovers the majority of the UMIs without costing too much sequencing data. To assess the capture efficiency of ultra-high probe density Salus-STS substrates, we compared Salus-STS with other NGS-based methods (Open-ST and Stereo-seq), as well as the commercial platform Visium HD by 10× Genomics. We ensured a fair comparison of capture ability by utilizing adult mouse brain data from Open-ST, Stereo-seq, and public data from 10× Genomics website (https://www.10xgenomics.com/datasets), each downsized to the same saturation level of ~0.5. Among these methods, Salus-STS demonstrated superior gene detection sensitivity (Fig. 1EF). We further evaluated the library diversity of Salus-STS data in comparison with other ST methods by calculating UMIs/bin (10 × 10 μm 2 ) and saturation relative to reads count (Fig. 1GH, note samples labeled as “MouseBrain” refer to “adult mouse brain”). These results highlight unparalleled capture capabilities of Salus-STS substrates as compared to other current spatial transcriptomic methods. Spatial cellular -type architecture of mouse testis We applied Salus-STS to a mouse testis tissue section and achieved high sensitivity of mRNA detection with a median of 13,834 UMIs and 4,199 genes per 10 × 10 μm² bin at a saturation level of 0.43. The UMI and gene numbers are higher than those typically reported in spatial experiments 8 (Fig. 2A, Fig. S4A). The spatial distribution of Uniform manifold approximation and projection (UMAP) clusters delineates the architecture of seminiferous tubules and the developmental stages of germ cells (Fig. 2B). To assess the consistency between our data and scRNA-seq data 27 , we integrated data from these two methods. We used RCTD for deconvolution, canonical correlation analysis (CCA) for batch integration, and UMAP for visualization (Fig. 2C). The adjusted rand index (ARI) 32 benchmarking metrics was used to assess the consistency between the annotation and the true labels 33,34 . Single-cell data was used as the reference and we calculated the adjusted rand indices of the Salus-STS and Slide-seq testis data. Salus-STS ARI cell type scored a relatively high value of 0.66 (as a comparison, Slide-seq scored 0.36), indicating that the clusters from Salus-STS data showed high similarity in transcriptomic profiles to those from scRNA-seq data. Notably, rare populations including myoid cells, macrophages and endothelial cells showed precise mapping to their scRNA-seq counterparts. This highlights the capability of Salus-STS to identify rare cell types. Furthermore, we mapped spatial distribution of major cell types in Fig.2D and Fig. S5. Cell annotation was further validated by examining the expression patterns of marker genes across different clusters (Fig. 2E). To highlight the importance of high UMI captures, we did a hypothetical experiment for testis tissue with different UMI levels. We subsampled the gene expression matrix to various UMI levels ranging from 14187 to 709 UMIs per 10 x 10 μm 2 bin, and computed cluster-specific differentially expressed genes (DEG, Supplementary Data 1). DEG numbers exhibited a strong positive correlation with UMI counts, demonstrating high UMI level is critical to unveil full transcriptomic landscapes. Furthermore, we compared cell-cell communication networks disclosed by Salus-STS and Slide seq (10 μm resolution, 668 UMIs/bin 10 x 10 μm 2 ). Implementing CellChat 35 , Salus-STS identified 3242 intercellular communication networks, while Slide-seq identified only 1 of them (Fig. S4B, Supplementary data 2). We further downsampled Salus-STS data to a level of median ~700 UMIs per bin. No effective intercellular communication was identified, which is consistent with the Slide-Seq data. This paradigm effectively demonstrates how enhanced transcript capture efficiency fundamentally transforms spatial system biology analyses. Further scrutinizing the data, we found some of our results are consistent with the literature and some are new. For example, it has been reported that seminiferous tubules are constructed by tight junctions of Sertoli cells 36 , which are located in the outermost layer of the tubules, consistent with our results. Within these tubules, we observed the different distributions of spermatogonia, spermatocytes, elongating spermatids, and round spermatids in the tubules, which align with our current understanding of mouse testis anatomy 37 . Additionally, our dataset identifies a broad range of somatic cells, including Leydig cells, endothelial cells, peritubular myoid cells, and macrophages (Fig.2D and Fig. S5). Peritubular myoid cells, which surround the seminiferous tubules, are typically found to be a single layer in rodent testes 38 . Due to their rarity, these cells are often underrepresented in scRNA-seq data 27 . Remarkably, our dataset clearly reveals the structure of peritubular myoid cells, exhibiting a latticework pattern (Fig.2D and Fig. S5). Such a structure has not been reported in the literature using similar methods, demonstrating that the spatial resolution and capture efficiency are crucial in locating rare cells and revealing their spatial organizations. Interestingly, in another area, we found macrophages and Leydig cells colocalized (lower left corner of Fig.2D and Fig. S5), together with enriched lymphocytes while sperm cells were absent. This possibly suggests a local inflammation incidence. We selected this region and a random control region from the testicular section and calculated the overall differential signaling pathways (Fig. S6A). GO enrichment analysis revealed that many of the top ranked, upregulated biological processes were linked to macrophage activation pathways (p.adjust < 0.001) and are related to major histocompatibility complex (MHC), indicating an upregulated antigen presenting activity of this area (Fig. S6B). It has been reported that macrophages and Leydig cells are functionally related and macrophages may produce cytokines that tune the steroidogenesis of the Leydig cell 39,40 . However, how this happens through a variety of signaling pathways is still largely unclear. High resolution, high capture efficiency spatial transcriptomics may suggest clues in solving these problems. Analysis of mouse testis architecture based on sub-cellular 2 × 2 μm 2 bins Given the large number of genes and UMIs captured, we analyzed the mouse testis dataset at 2 μm resolution. A ~0.7 × 0.7 mm² area was selected, encompassing approximately seven seminiferous tubules. Using 2 × 2 μm² bins, we identified a median of 215 genes and 333 UMIs (Fig. 3A). We annotated each 2 × 2 μm 2 bin using RCTD method (Fig. 3B), revealing the seminiferous tubules and layered structures inside the tubules. The spatial distributions of single cell types revealed more details about the tissue structure (Fig. 3C), allowing us to observe islands of bins with sizes similar to a cell. For example, in Fig. 3C-iv, we see islands of bins which are likely individual macrophages. It has been reported that the transcriptome of Sertoli cells is closely associated with stages of seminiferous epithelial cycles 41,42 . Our spatial architecture demonstrates high resolution Sertoli cell distribution that different Sertoli cells from adjacent seminiferous tubules can be well-separated (Fig. 3C-vi). The high-resolution cell maps undoubtedly will facilitate the study of Sertoli cell-germ cell interactions. We quantified the spatial distances between RCTD-annotated cell types and their marker genes (see Methods, Fig. 3D). The distance between elongating spermatids and their markers was well below 1 μm, indicating their colocalization. As a contrast, spermatocytes showed over10-fold greater distance to the elongating spermatid markers. We also profiled marker gene expression matrix of across cell types (Fig. 3E). These results together demonstrate convincing cell type recognition. Note that RCTD, a deconvolution algorithm resolving cell compositions within "spots" containing multiple cells with mixed types, may not be ideal for the analysis of subcellular sized 2 μm bins. To avoid this problem, we tested to analyze the data using unsupervised clustering (Fig. S7A). The tissue architecture is similar to that disclosed by RCTD. Interestingly, expression profiles of clusters displayed a distinct pattern characterized by the absence of a highlighted “diagonal line” (Fig. S7B). This is possibly caused by intracellular heterogeneity: 2 × 2 μm 2 bins capture molecular signatures from distinct subcellular compartments rather than from whole cells. In fact, it is unclear to us so far how to best use the high resolution, high sensitivity data. However, current reasonable results and new structures disclosed suggest that subcellular level spatial transcriptomics is possible. It invites new ideas to dig out rich information buried in these data. Spatially resolved whole transcriptome of mouse brain To illustrate the in situ capturing ability of Salus-STS, we analyzed an adult mouse hemibrain tissue section. The spatial heatmaps of UMIs and genes reveal the anatomical structure of the mouse brain section (Fig. 4A). Our dataset achieved a high detection sensitivity for mRNAs, with a median of 2,851 UMIs and 1,340 genes at a saturation of 0.46 for 10 × 10 μm² bins (Fig. 4B), higher than previous reports 13–15 . For examples, Open-ST captured 246 UMIs and 166 genes per 10 × 10 μm² bins at a saturation level > 0.9 (calculated from the published dataset). Nova-ST reported 349 UMIs and 199 genes for 10 × 10 μm² bins (no saturation reported) for adult mouse brain. For StereoSeq, the original manuscript did not report these values; however, multiple researchers calculated from the published dataset that the UMIs for StereoSeq are between 300~500 per 10 × 10 μm² bin at a saturation of > 0.9. We confirmed above calculations and concluded that current sequencing-based methods usually detect UMIs in lower hundreds per10 × 10 μm² bin for adult mouse brain, which is lower than that of Salus-STS. To showcase the high-definition transcriptome map disclosed by the data, we visualized the spatial distributions of specific marker genes in the hippocampus and cortical areas (Fig. 4C): P antr1, and Wfs1 for CA1, Rgs14 and Sv2b for CA2, Tspan18 and Il16 for CA3, Prox1 and Stxbp6 for DG. Additionally, a series of cortical markers exhibit layer-specific distribution patterns (Fig. 4D). Impressively, the distribution of Ccn2 clearly reveals the structure of L6b sublayer. Overall, we observed a consistent correspondence between the marker distributions and their respective anatomical regions, closely resembling the ISH data from the Allen Brain Atlas 43 . To conclude, using 10 × 10 μm² bins as the elements of analysis and RCTD annotations with a scRNA-seq dataset as the reference 28 , we precisely reconstructed the cellular map of a mouse brain section, identifying 29 subclasses of glutamatergic neurons (Fig. S8), 6 sub-classes of GABAergic neurons (Fig. S9A), and 6 subclasses of non-neuronal cells (Fig. S9B). 41 out of 42 subclasses in the reference dataset were successfully characterized, demonstrating the ability of Salus-STS to identify major and rare cell sub-types. The mouse cortical area comprises diverse neurons and non-neuronal cells of various types and sizes. To better visualize the cellular landscape, we selected an area from the cortex and performed cell segmentations (i.e., cellbins, see Next section). RCTD annotations successfully mapped 14 distinct cell types, including 7 glutamatergic neuron subtypes (Car3, L2/3, L4, L4/5, L5, L5/6, L6), which shows layer-specific distributions, 4 GABAergic neuron subtypes (Lamp5, Pvalb, Sncg, SST), and 3 non-neuronal types (astrocytes, microglia, and oligodendrocytes) (Fig. 4E). Our dataset clearly reveals the spatial relationships between neurons and non-neuronal cells, which are crucial in cell-cell interactions and communications, such as juxtacrine and paracrine signaling. We conducted a comparative analysis of Salus-STS and Stereo-seq in the hippocampal region from mouse brain data. Through evaluating a series of markers, Salus-STS demonstrated superior performance in both the proportion of marker-positive cells and the quantitative expression of these markers, indicated enhanced sensitivity (Fig. S10A). Furthermore, we performed integrated analysis combining scRNA-seq data with spatial data from both platforms (Fig. S10B). UMAP visualization reveals strong concordance in distribution patterns for major cell types such as Glutamatergic DG, CA3 and CA1. Notably, Salus-STS exhibited improved resolution for rare cell populations, e.g., non-neuronal endothelial cells and astrocytes, achieving clear cluster separation. In contrast, Stereo-seq data showed substantial overlap between these cell types. Mouse brain analysis using Salus Cellbins Fig. 5 shows another example of mouse brains, whose anatomic structure includes the cortex, hippocampus and thalamus, can be accurately identified using unsupervised clustering of 25 × 25 μm 2 bins (Fig. 5A). In this case, we analyzed the hemibrain using our cell segmentation algorithm (Salus Cellbins), which recognizes cell nuclei based on unspliced mRNA distribution and employs a watershed algorithm to define cell boundaries (Fig. 5B). The cell boundaries were highly consistent with the H&E staining images (Fig. S11A). We further examined the cell segmentation results by selecting the mouse hippocampal region, implementing both H&E imaging-based and unspliced mRNA-based methods. Both methods showed excellent cell type-marker gene correlation, validated by their gene expression matrixes (Fig. S11B, C). Notably, cell segmentation improved clustering quality in the hippocampus area by clearly distinguishing different regions as compared to the original 25 × 25 μm 2 bin result (Fig. 5C, D). It has been reported CA2 area resembles a terminal portion of CA3 region 44 , thus is often recognized as a part of CA3. However, CA2 exhibits distinct molecular and functional properties, as well as unique connectivity patterns that may be relevant to disease mechanisms 45 . Leveraging cell segmentation and annotation, Salus-STS successfully resolved the hippocampal subdivisions, including all DG, CA1, CA2, and CA3 (Fig. 5D) regions. Furthermore, annotations of segmented cells by RCTD 46 revealed a high-definition cellular landscape in the mouse hippocampus, including clearly separated cortical layers (Fig. 5E). The cell annotation was rigorously verified by comparing the expression patterns of known cell markers across different cell types (Fig. 5F). Lateral molecular diffusion The spatial accuracy of mRNA capture is a critical aspect of spatial transcriptomic methods, typically quantified by the lateral molecular diffusion of mRNAs with known expression patterns. To evaluate this, we profiled the spatial transcriptome of mouse olfactory bulb (MOB). Unsupervised clustering with UMAP at a bin size of 10 × 10 μm 2 was performed. Clusters were annotated based on expression profiles of specific markers, which aligns well with tissue anatomy from the Allen Brain Atlas 43 (Fig. 6A). To evaluate molecular diffusion, we selected three neuronal cell-type-specific marker genes (Calb2, Cdhr1, and Slc17a7), which form distinct stripes near the mitral cell layer (MCL) 47 and examined their spatial distributions for different ST methods. We focused on the concentration level of each gene by selecting a 4-mm wide region where the MCL located in the middle (Fig. 6B, C), then calculated the mean concentration level at every 10 μm interval (Fig. 6D), hypothesizing that good control of RNA diffusion would produce a sharp peak in the spatial profile. For Salus-STS and Stereo-seq, all 3 genes exhibited sharp peaks near the MCL. In contrast, Pixel-seq and Slide-seq data showed less pronounced peaks, possibly suggesting low capture numbers and greater RNA dispersion caused by diffusion. The diffusion was also accessed by using mouse hippocampal markers (Fig. 6E). Lct, Prox1, and Stxbp6 were selected because of their specific expressions in the DG 48 region. Salus-STS showed superior control of diffusion, characterized by sharp peaks in the spatial profile among three ST methods (Fig. 6F). Discussion Spatial transcriptomic methods have emerged as a powerful tool in biomedical research 49–51 . Despite of the technological advancements over the past decade, there still lacks a method that offers simultaneous high resolution, sensitive gene detection, and whole transcriptomic profiling. Some recent sequencing-based methods give a spatial resolution for capture addresses between 0.5 ~ 2 μm, well below the size of a biological cell. However, due to poor detection efficiency, the data have to be binned to have a size similar to or larger than a cell for further analysis. The advantage of subcellular address resolution cannot be fully exploited. Even after binning, the total number of genes and UMIs that are captured in a voxel equivalent in size to a cell are ~one order of magnitude smaller than those from single cell RNA sequencing. This introduces inaccuracy and uncertainty in subsequent data analysis. In this study, we introduce Salus-STS, a sequencing-based method that relies on solid-phase capture arrays. Salus-STS has three key features: (1) high spatial resolution for capture addresses of ~1 μm; (2) ultra-high probe density of ~55,000 +/- 10,000 probes/μm 2 , enabling efficient whole transcriptome capture and characterization; (3) flexible tuning of the molecular recognition portions of the capture probes, providing versatility for studying different types of targets. In studying adult mouse testis and brain, Salus-STS demonstrated the highest sensitivity in the literature to the best of our knowledge. It captured a median of 13,834 UMIs and 4,199 genes per 10 × 10 μm² bin at a saturation of 0.43 for mouse testis, and a median of 2,851 UMIs and 1,340 genes at a saturation of 0.46 for adult mouse brain. These numbers are much higher than literature reports 13–15 , making Salus-STS approaching scRNA-seq in detection sensitivity. The high detection efficiency allowed us to analyze gene expression profiles for mouse testis with 2 × 2 μm 2 bins, elements much smaller than a biological cell. Reasonable and new results about tissue architecture were obtained, which demonstrates that Salus-STS has great potential pushing sequencing-based spatial transcriptomics toward real sub-cellular resolutions 52 . In summary, we envision Salus-STS as a powerful tool that provides both high spatial resolution and high throughput for advancing research in life sciences and translational medicines. Declarations Supplementary Information Four supplementary figures are in the Supplementary Materials. Codes and Data availability Testis single cell RNA data at the NCBI under GEO accession number GSE112393. Mouse brain reference single cell data generated by Allen Institute for Brain Science are available at web portal (https://portal.brain-map.org/atlases-and-data/rnaseq). Salus-STS gene expression matrix are available at the Google Drive: (https://drive.google.com/drive/folders/10krNQShSm7E3bgoM__cqxBspR-YSqihr?usp=sharing). OpenST adult mouse hippocampus and E13 head are available at web portal (https://rajewsky-lab.github.io/openst/examples/getting_started/). Stereo-seq mouse brain at CNGBdb under experiment ID (CNX0422300). Stereo-seq mouse brain v1.3 and Stereo-seq mouse brain cellbin data download from STOmics (https://en.stomics.tech/col1241/index.html, https://en.stomics.tech/col1311/index.html). Slide-seq mouse testis data download from https://www.dropbox.com/s/ygzpj0d0oh67br0/Testis_Slideseq_Data.zip?dl=0. A standard pipeline is available for converting fastq files to bin-segmented expression matrices. The download link for the standard pipline is https://github.com/xuzaoxu/SalusSTS. Acknowledgements This work is supported by Shenzhen Science and Technology Program “KJZD20230923114220041”. Author contribution GW, EL, LZ, YB, and RL conceived the project. GW, EL, LZ, YB, DFu, RL, and ZX supervised the whole project and designed the experiments. RL, DFu, HZ, and DFeng designed the Salus-STS capture substrates and cassettes. XZ, YiC, JC, and XL performed the majority of the experiments. ZX and HX designed bioinformatic pipelines. HL, CL, and ZX analyzed the data. GL participated in designing functional oligos. QC optimized enzymes for the biochemical reactions. WC, YC, LC，SX, CZ, YL, HW, and TF customized the DNA sequencer for spatial transcriptomics studies. DC synthesized modified dNTPs materials for the biochemical reaction. RL, ZX, DFu and GW wrote the manuscript. All authors read and approved the manuscript. Author information All authors are employees of Salus BioMed Inc. Ltd. Correspondence and requests for materials should be addressed to [email protected] . References Angerer, L. M. & Angerer, R. C. Detection of Poly A + RNA in Sea Urchin Eggs and Embryos by Quantitative in Situ Hybridization . Nucleic Acids Research vol. 9 https://academic.oup.com/nar/article/9/12/2819/2380280 (1219). Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 5 , 877–879 (2008). Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. 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Supplementary Files SupplementatyData1.xlsx Supplementary data 1 SupplementaryData2.xlsx Supplementary data 2 SalusSTSNBTSIFinal.docx Supplementary Information reportingsummary.pdf reporting summary Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7337510","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508971332,"identity":"5d5d9ec8-741f-4ea2-b1d6-14e1ac3b6d63","order_by":0,"name":"Gufeng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYNACGxsGBnYgzdggQayWtDQGBmYStRyGaSFCscHxs4dffkk4b8/fzGP8gXGHhRwD++GjG/BqOZOXZi2TcDtxxmEeMwnGMxLGDDxpaTfwaTE7kGNmLPnjdgIDUAvz3zaJxAYJHjP8Ws6/MTOWSDhnL38Y5DCitNzIMX74IeEA44bDPAYSRGmxv/HGjJkhITlx42G2MrBf2Aj5RbI/x/jjjwQ7e7njzZuBIVYnx89++BheLUDAJs2DwiWgHASYP/4gQtUoGAWjYBSMYAAAgFdHD0IJCDcAAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen Salus BioMed","correspondingAuthor":true,"prefix":"","firstName":"Gufeng","middleName":"","lastName":"Wang","suffix":""},{"id":508971333,"identity":"a351afca-42b1-4c08-9a8c-7d634c07dcd4","order_by":1,"name":"Renjie Liao","email":"","orcid":"","institution":"Shenzhen Salus Biomed Inc. 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Ltd","correspondingAuthor":false,"prefix":"","firstName":"Luyang","middleName":"","lastName":"Zhao","suffix":""},{"id":508971358,"identity":"3418e84a-11c9-4d4b-8ebd-2b1f6f60b941","order_by":26,"name":"Erkai Liu","email":"","orcid":"","institution":"Shenzhen Salus Biomed Inc. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Erkai","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-10 08:05:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7337510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7337510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90488682,"identity":"dab79a5b-6aaa-4b7a-aac7-80daadf2ce4b","added_by":"auto","created_at":"2025-09-03 09:18:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":949409,"visible":true,"origin":"","legend":"\u003cp\u003eSalus-STS capture substrate design and characterization. (A) Schematics illustrating the generation of high-density clusters of the stalk portion of the capture probes. Scale bar: 10 μm. SBC: Spatial barcodes; bPCR: bridge polymerase chain reaction. (B) Attachment of molecular recognition portion to capture probe stalks. (C) Comparison of center-to-center distance of different methods in the literature. (D) Comparison of reported probe densities. (E) Boxplots showing the number of UMIs detected at 10 μm resolution by Salus-STS with Open-ST, Stereo-seq and Visium HD. All from adult mouse brain data at the same saturation level. (F) Boxplots showing the number of genes detected at 10 μm resolution for the same samples in (E). (G) UMIs/bin (10 × 10 μm\u003csup\u003e2\u003c/sup\u003e) as a function of reads count across multiple types of tissue for different ST methods. Mouse testis and brain data are discussed in the manuscript. Mouse liver data are shown in Fig. S3. (H) Saturation as a function of reads count of the same samples in (G).\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/c0e2d584de91c7962736cc73.png"},{"id":90488689,"identity":"5659a116-0c69-4004-be8f-a55266ca4599","added_by":"auto","created_at":"2025-09-03 09:18:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2098483,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial cellular architecture of mouse testis. (A) Violin plots of number of UMIs and genes detected by Salus-STS (10 × 10 μm\u003csup\u003e2\u003c/sup\u003e bins) and scRNA-seq (per cell). (B) Spatial mapping of clusters from UMAP analysis. Scale bar: 1mm. (C) UMAP analysis of Salus-STS dataset with scRNA-seq data. SPG: spermatogonia; Scytes: meiotic spermatocytes; Stids: postmeiotic haploid round spermatids; Elongating: elongating spermatids. (D) Spatial distributions of different cell types annotated by RCTD using 10 × 10 μm\u003csup\u003e2\u003c/sup\u003e bins. (E) Expression pattern of specific marker genes across major cell types from mouse testis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/39831382bf4312dba35bb348.png"},{"id":90488683,"identity":"6045764c-f0ea-4d9a-b15c-604be2c6b456","added_by":"auto","created_at":"2025-09-03 09:18:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2662281,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of mouse testis architecture based on sub-cellular 2 × 2 μm\u003csup\u003e2\u003c/sup\u003e bins. (A) Violin plots for numbers of UMIs and genes detected per bin. (B) Spatial distribution of cell types for each bin annotated with RCTD analysis. Scale bar: 200 μm. (C) Spatial distribution of cell types for each bin annotated with RCTD analysis, same area as (B). (D) Spatial distances between elongating spermatid marker genes and elongating spermatids (Elongating), and between elongating spermatid marker genes and spermatocytes (Sytes_Elongating). (E) Expression pattern of specific marker genes across RCTD annotated results.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/84580dc00ef35485141200b3.png"},{"id":90489999,"identity":"13951c4c-74f7-4d22-b4b1-f26cadf3d10c","added_by":"auto","created_at":"2025-09-03 09:26:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2828499,"visible":true,"origin":"","legend":"\u003cp\u003eSpatially resolved whole transcriptome of mouse brain. (A) Spatial heatmap of UMIs and genes captured across the mouse brain section. (B) Violin plots of UMIs and genes detected. (C) Spatial distributions of hippocampal markers detected by Salus-STS (right), compared with ISH images (left). (D) Spatial distributions of specific cortical layer markers detected by Salus-STS and compared with ISH images. (E) Spatial distribution of different cell types in the cortical region annotated by RCTD using cellbins.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/fec51898ab6e531be5475d02.png"},{"id":90490000,"identity":"2347bcb3-be26-4d46-9212-d52940e87ac0","added_by":"auto","created_at":"2025-09-03 09:26:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2501473,"visible":true,"origin":"","legend":"\u003cp\u003eWhole transcriptome of mouse brain analyzed with Salus Cellbins. (A) Unsupervised clustering of the mouse hemibrain section using 25 × 25 μm\u003csup\u003e2\u003c/sup\u003e bins. Bins are colored by their annotation. CA1: cornu ammonis; COA: cortical amygdalar area; CP: caudoputamen; CTX: cortex; DG: dentate gyrus; HY: hypothalamus; MEAad: medial amygdalra nucleus, anteroventral part; MEAav: medial amygdalar nucleus, anterodorsal part; PIR: pifiform area; sAMY: stiatum-like amygdalar nuclei; VP: ventral posterior complex. (B) Visualization of segmented cell boundaries. (C) UMAP plots of 25 × 25 μm\u003csup\u003e2\u003c/sup\u003e bins and spatial mapping of the clusters in the red frame from (A). (D) UMAP plots of segmented cells and spatial mapping of the clusters. (E) Segmented cells annotated by RCTD. (F) Expression pattern of specific marker genes across different cell types from hippocampus.\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/213daa803756d151dfa5d3ed.png"},{"id":90489997,"identity":"a2280fa8-0529-4646-91b3-4eb1b8cf8370","added_by":"auto","created_at":"2025-09-03 09:26:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2184066,"visible":true,"origin":"","legend":"\u003cp\u003eLateral molecular diffusion analysis. (A) Unsupervised clustering and annotations of mouse olfactory bulb tissue section processed by Salus-STS (left). Refence of mouse olfactory bulb anatomy from Allen brain atlas (right). (B) Selected region for diffusion analysis, with the red line indicating MCL (left), yellow to purple regions representing the selected 4-mm width region, and the color gradient representing the distance from the red line (right). (C) Selected regions for diffusion analysis from mouse olfactory bulb across different ST methods (Pixel-seq, Slide-seq, and Stereo-seq). (D) Mean detection of\u003cem\u003e Calb2, Cdhr1, and Slc17a7 \u003c/em\u003eacross the selected regions. (E) Selected regions for diffusion analysis from DG across different ST methods (Visium HD, Stereo-seq and Salus-STS). (F) Mean detection of\u003cem\u003e Lct, Prox1, and Stxbp6 \u003c/em\u003eacross the selected region.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/62e12d55414805b3cd968870.png"},{"id":101942998,"identity":"4003b0cc-4a32-4df7-9dbb-3214058c81d6","added_by":"auto","created_at":"2026-02-05 09:39:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19266887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/8fdfb623-7e23-4e3a-8e12-21acfb0bdd6e.pdf"},{"id":90488680,"identity":"fe56d459-c90d-408e-8f4b-f48b2016711b","added_by":"auto","created_at":"2025-09-03 09:18:01","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11758,"visible":true,"origin":"","legend":"Supplementary data 1","description":"","filename":"SupplementatyData1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/e947449a70a562fb2f1c614e.xlsx"},{"id":90489996,"identity":"506f5472-deb3-4643-9456-6ef6d9cc4966","added_by":"auto","created_at":"2025-09-03 09:26:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11853,"visible":true,"origin":"","legend":"Supplementary data 2","description":"","filename":"SupplementaryData2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/e8f7ddc0ffb9fc51174e68da.xlsx"},{"id":90488686,"identity":"dccaf419-216c-437d-ab37-c344d65ae37d","added_by":"auto","created_at":"2025-09-03 09:18:01","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4557944,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SalusSTSNBTSIFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/7fb63872a1c5595900928cff.docx"},{"id":90490365,"identity":"6362c909-92cb-44d8-bfe0-83012ffa5ef4","added_by":"auto","created_at":"2025-09-03 09:34:01","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1665197,"visible":true,"origin":"","legend":"reporting summary","description":"","filename":"reportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7337510/v1/1e494d92714289b7ac25494b.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nAll authors are employees of Salus Biomed Inc. Ltd.","formattedTitle":"Sequencing-based Spatial Transcriptomics with High Sensitivity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIt has been long ever since researchers started to explore gene expression using RNA transcripts at cell- and tissue-levels\u003csup\u003e1\u003c/sup\u003e. Initial efforts included those hybridization probe-based imaging methods, like fluorescence in situ hybridization (FISH)\u003csup\u003e2\u003c/sup\u003e. Not until very recently can this approach be called spatial transcript-OMICS (e.g.,\u0026nbsp;MERFISH\u003csup\u003e3\u003c/sup\u003e, seqFISH\u003csup\u003e4\u003c/sup\u003e and STARmap\u003csup\u003e5\u003c/sup\u003e) when a large panel of genes (over several hundred) can be studied in one experiment. However, these methods rely on preliminary knowledge and well-designed panels, limiting their roles in discovery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sequencing-based spatial transcriptomics methods, on the other hand, capture the whole genome expression profile using spatially barcoded probes. For this reason, these techniques are also\u0026nbsp;referred to as \u0026ldquo;unbiased methods\u0026rdquo;. A group of techniques emerged, such as\u0026nbsp;Visium\u003csup\u003e6\u003c/sup\u003e, Slide-seq\u003csup\u003e7,8\u003c/sup\u003e,\u0026nbsp;HDST\u003csup\u003e9\u003c/sup\u003e,\u0026nbsp;DBIT\u003csup\u003e10\u003c/sup\u003e, Decoder-Seq\u003csup\u003e11\u003c/sup\u003e, Seq-Scope\u003csup\u003e12\u003c/sup\u003e, Stereo-seq\u003csup\u003e13\u003c/sup\u003e,\u0026nbsp;Open-ST\u003csup\u003e14\u003c/sup\u003e, Nova-ST\u003csup\u003e15\u003c/sup\u003e, PixelSeq\u003csup\u003e16\u003c/sup\u003e, Visium HD\u003csup\u003e17\u003c/sup\u003e, which drastically expedited the advance of relevant fields. So far, they have shown their ability to\u0026nbsp;determine cell-type architecture of tissue, reveal cell\u0026ndash;cell interactions, and monitor cell differentiation and tissue development, etc.\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e Some of these methods\u0026rsquo; spatial resolution for capture addresses is improved to be\u0026nbsp;0.5~2 \u0026mu;m\u003csup\u003e21\u003c/sup\u003e, sufficiently high to support cell-level studies.\u003c/p\u003e\n\u003cp\u003eHowever, current sequencing-based methods exhibit limited capture efficiency of mRNA molecules. While a mammalian cell typically contains 10\u003csup\u003e5\u003c/sup\u003e\u0026ndash;10\u003csup\u003e6\u003c/sup\u003e mRNA molecules, and expresses up to 10,000 different genes\u003csup\u003e22\u003c/sup\u003e, spatial transcriptomics experiments usually report the detection of several hundred mRNAs per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area on a capture chip, a size similar to that of a cell, at a reasonable sequencing saturation. For examples, Chen et al. utilized Slide-seq to investigate spermatogenesis and mechanisms of diabetes-induced male infertility, achieving a mean detection level of 784 UMIs per bead (10 \u0026mu;m diameter)\u003csup\u003e8\u003c/sup\u003e. Xu et al. explored mouse liver cell functions and molecular modulations during liver regeneration using Stereo-seq\u003csup\u003e23\u003c/sup\u003e, capturing a median of ~800 UMIs per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area (Note different studies used differently binned areas for analysis. For the sake of comparison, all reported UMIs are normalized according to a unit area of 10 \u0026times; 10 \u0026mu;m\u0026sup2;). Similarly, Seq-Scope was employed to study the homeostatic liver, achieving a capture efficiency comparable to Stereo-seq (median ~800 UMIs per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area)\u003csup\u003e12\u003c/sup\u003e. Visium (10\u0026times; Genomics) has been widely used in various type of tissue, such as mouse pulmonary fibrosis (median ~30 UMIs per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area)\u003csup\u003e24\u003c/sup\u003e, damage healing of mouse intestine (average ~120 UMIs per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area)\u003csup\u003e25\u003c/sup\u003e, and mouse organogenesis\u003csup\u003e26\u003c/sup\u003e (median ~220 UMIs per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area). The gene expression level varies hugely in different types of tissue. The reported mRNA detection in these applications is usually in the range of several tens to several hundred UMIs per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area. As a comparison, single cell RNA sequencing (scRNA-seq) experiments typically report the capture of several thousand mRNA molecules in a cell\u003csup\u003e2728\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e, ~ one order of magnitude higher. Note that this difference in the amounts of captured mRNAs is NOT merely caused by different sequencing depths as both methods normally sequence the DNA library to a reasonable saturation level (0.5-0.7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn accurate gene expression profile is essential for subsequent bioinformatics analysis. The greater the number and variety of transcripts that can be detected, the more comprehensive and accurate the transcriptomic state of a tissue is. Low capture efficiency diminishes the effort of improving the capture address resolution because the data has to be \u0026ldquo;binned\u0026rdquo; to a poorer resolution so that there are sufficient UMIs in each unit and they can be analyzed. Furthermore, it has several profound negative impact on the data interpretation: (1) difficulty in cell type identification and classification; (2) missing rare cell types and sub-types; (3) missing low-abundance genes that may play a key role in biological processes; (4) inaccurate cell-cell interactions due to missing expressed genes; (5) limited resolving power for dynamic gene expression changes upon external perturbation; (6) susceptible to background noises such as non-specific binding, sequencing error, etc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this manuscript, we present experimental setup and most recent data from a new spatial transcriptomic system (Salus-STS). It provides ~1 \u0026mu;m capture resolution and the surface capture probe density is raised to ~55,000/\u0026mu;m\u0026sup2;, which yields a capture efficiency approaching that of scRNA-seq techniques. We show that per 10 \u0026times; 10 \u0026mu;m\u0026sup2; area , we are able to capture ~14,000 UMIs from mouse testis sample with a saturation of 0.43, ~2,800 UMIs from adult mouse brain sample with a saturation of 0.46, and ~5,000 UMIs from mouse liver with a saturation of 0.70. To the best of our knowledge, these are the highest number of captures that has been reported for ST experiments in the literature. Finally, we demonstrate that such a high capture efficiency allows us to analyze the data at an effective resolution of 2 \u0026mu;m, which discloses fine tissue structures that are challenging to observe with other methods.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCapture substrate design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsider an equilibrium surface binding reaction: \u003cem\u003eS\u003c/em\u003e + \u003cem\u003eA\u003c/em\u003e = \u003cem\u003eSA\u003c/em\u003e, where \u003cem\u003eS\u003c/em\u003e stands for the surface binding site, \u003cem\u003eA\u003c/em\u003e stands for the target molecule being captured, \u003cem\u003eSA\u003c/em\u003e stands for the target-surface site complex. In ST experiments, only a small portion of probes (less than 1%) bound to the mRNA targets, so the depletion of the surface binding site \u003cem\u003eS\u003c/em\u003e is negligible. In this case, the surface concentration of captured targets can be approximated as \u003cem\u003e[SA]=[A]\u003csub\u003e0\u003c/sub\u003e/(1+1/Kc)\u003c/em\u003e, where [\u003cem\u003eSA\u003c/em\u003e] and [\u003cem\u003eA\u003c/em\u003e]\u003cem\u003e\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e stand for the surface concentration of reacted and initial target molecules, respectively, \u003cem\u003ec\u003c/em\u003e is the initial concentration of surface binding site \u003cem\u003eS\u003c/em\u003e in the solution, and \u003cem\u003eK\u003c/em\u003e is the binding constant. Before \u003cem\u003eKc\u003c/em\u003e reaches a large enough value (0.1), \u003cem\u003e[SA]\u003c/em\u003e\u003cem\u003e\u0026asymp;\u003c/em\u003e\u003cem\u003e[A]\u003csub\u003e0\u003c/sub\u003e * Kc\u003c/em\u003e, it is clear that the amount of the mRNAs captured is directly proportional to the initial surface concentration of the capture sites. Thus, the key to improve mRNA capture efficiency is to increase the amount of capture probes per unit area.\u003c/p\u003e\n\u003cp\u003eBearing this in mind, we designed Salus-STS capture substrate\u0026nbsp;as depicted in Fig. 1. A next generation DNA sequencer Salus Pro (Salus BioMed) operating under the sequencing-by-synthesis (SBS)\u0026nbsp;principle\u003csup\u003e30,31\u003c/sup\u003e served as the platform for generating ultra-high-density capture probes on substrate with random spatial barcodes (SBCs), and for decoding the SBCs. Note that there are recent efforts using Illumina sequencing flowcells to fabricate the capture substrates, e.g., Seq-Scope\u003csup\u003e12\u003c/sup\u003e, Open-ST\u003csup\u003e14\u003c/sup\u003e, Nova-ST\u003csup\u003e15\u003c/sup\u003e, etc. However, since they had to use the original Illumina sequencing flowcell surface, their capability in optimizing the experiments is largely hindered. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we designed the capture substrate so that it is compatible with a Salus Pro DNA sequencer. The capture areas are either 8.6\u0026times;8.6 mm\u003csup\u003e2\u003c/sup\u003e or 11\u0026times;11 mm\u003csup\u003e2\u003c/sup\u003e. The primers on the surface for bridge amplification were redesigned so they would not interfere with standard Illumina library construction. The stalk portion of the capture probes contained\u0026nbsp;a random 30-bp nucleotide sequence\u0026nbsp;serving as the spatial barcode, which\u0026nbsp;holds\u0026nbsp;excess complexity (over 10\u003csup\u003e18\u003c/sup\u003e) for spatial mapping\u0026nbsp;over an area larger than 1 cm\u003csup\u003e2\u003c/sup\u003e. In the fabrication of the capture substrate, the stalks of the capture probes were bridge amplified on the substrate with optimized primer surface density.\u0026nbsp;Through multiple rounds of\u0026nbsp;amplification, clusters of the stalks formed with a surface density of ~1 cluster/\u0026mu;m\u003csup\u003e2\u003c/sup\u003e, equivalent to a center-to-center distance of ~1 \u0026mu;m (Fig. 1A). In this setup, the stalks of the capture probes were randomly hybridized on the surface, leading to randomly distributed probes. Subsequently, the Salus Pro DNA sequencer was employed to decode random SBCs for each physical address on the substrate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing decoding, the molecular\u0026nbsp;recognition portions of the\u0026nbsp;probes were\u0026nbsp;added to the stalks with a guided extension approach\u0026nbsp;(Fig. 1B). These recognition portions\u0026nbsp;consist of unique molecular identifiers (UMIs) and polyT sequences. UMIs are random 10-bp nucleotides serving to distinguish transcripts of the same gene locus captured by the same\u0026nbsp;cluster of probes. PolyT sequences\u0026nbsp;were used to\u0026nbsp;capture mRNAs in an unbiased manner\u0026nbsp;through hybridization with mRNAs\u0026rsquo; polyA tails.\u0026nbsp;The capture surface prepared this way has a spatial capture resolution of ~1 \u0026mu;m (Fig. 1C); the capture probe density on the surface was maximized to be\u0026nbsp;55,000 +/- 10,000\u0026nbsp;/\u0026mu;m\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(see Methods, Fig. S1), much higher than those current methods\u003csup\u003e6\u003c/sup\u003e (Fig. 1D). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCapture saturation and efficiency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the substrate fabrication step are the standard protocols for sequencing-based spatial transcriptomics, which can be completed in any biological research lab equipped with a cryo- microtome. The standard protocols include frozen tissue micro-sectioning, tissue slice being mounted on the substrate, fixation, permeabilization, and mRNA capturing by the probes. Subsequently, reverse transcription and second-strand synthesis are performed. The second strands are then denatured, washed off, and collected. The resulting cDNAs are utilized for library construction and subjected to paired-end sequencing in conjunction with SBCs. Through the alignment of each cDNA to the SBC map, a high-resolution spatial transcriptomic landscape is constructed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn ST experiments, the concept of sequencing saturation is introduced to help researchers evaluate whether the ST library has been sequenced to sufficient depth. Sequencing Saturation = 1 - (unique reliable alignment reads/all reliable alignment reads). Generally, when the number of sequencing reads is increased, one obtains more UMIs. However, the increasing rate of the UMIs slows down drastically as the sequencing saturation approaching 1. To give a clearer picture, we simulated the relationships between reads count, UMIs and saturations at different level of capture efficiency (see Methods). In the simulation, a total number of 5 million, 10 million, 20 million RNAs per mm\u003csup\u003e2\u003c/sup\u003e are captured, respectively (Fig. S2). Notably, if the capture efficiency of a ST experiment is low, the recovered UMIs and saturation level will level off at relatively low sequencing reads (Fig. S2A), which means further increasing the sequencing depth will NOT recover more UMIs. Since ST libraries are rarely sequenced to be above 0.9 saturation due to sequencing cost concerns, and actual sequencing depth varies in different studies, a fair comparison for different methods is to compare UMIs at the same sequencing saturation level (Fig. S2B, C). Clearly, the larger number of UMIs recovered, the higher capture efficiency it indicates. A proper range of saturation level of ST experiments will be 0.5 to 0.7, as it recovers the majority of the UMIs without costing too much sequencing data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess the capture efficiency of ultra-high probe density Salus-STS substrates, we compared Salus-STS with other NGS-based methods (Open-ST and Stereo-seq), as well as the commercial platform Visium HD by 10\u0026times; Genomics. We ensured a fair comparison of capture ability by utilizing adult mouse brain data from Open-ST, Stereo-seq, and public data from 10\u0026times; Genomics website (https://www.10xgenomics.com/datasets), each downsized to the same saturation level of ~0.5. Among these methods, Salus-STS demonstrated superior gene detection sensitivity (Fig. 1EF). We further evaluated the library diversity of Salus-STS data in comparison with other ST methods by calculating UMIs/bin (10 \u0026times; 10 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e) and saturation relative to reads count (Fig. 1GH, note samples labeled as \u0026ldquo;MouseBrain\u0026rdquo; refer to \u0026ldquo;adult mouse brain\u0026rdquo;). These results highlight unparalleled capture capabilities of Salus-STS substrates as compared to other current spatial transcriptomic methods.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial cellular\u003c/strong\u003e\u003cstrong\u003e-type architecture of mouse testis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied Salus-STS to a mouse testis tissue section and achieved high sensitivity of mRNA detection with a median of 13,834 UMIs and 4,199 genes per 10 \u0026times; 10 \u0026mu;m\u0026sup2; bin at a saturation level of 0.43. The UMI and gene numbers are higher than those typically reported in spatial experiments\u003csup\u003e8\u003c/sup\u003e (Fig. 2A, Fig. S4A). The spatial distribution of Uniform manifold approximation and projection (UMAP) clusters delineates the architecture of seminiferous tubules and the developmental stages of germ cells (Fig. 2B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess the consistency between our data and scRNA-seq data\u003csup\u003e27\u003c/sup\u003e, we integrated data from these two methods. We used RCTD for deconvolution, canonical correlation analysis (CCA) for batch integration, and UMAP for visualization (Fig. 2C). The adjusted rand index (ARI)\u003csup\u003e32\u003c/sup\u003e benchmarking metrics was used to assess the consistency between the annotation and the true labels\u003csup\u003e33,34\u003c/sup\u003e. Single-cell data was used as the reference and we calculated the adjusted rand indices of the Salus-STS and Slide-seq testis data. Salus-STS ARI cell type scored a relatively high value of 0.66 (as a comparison, Slide-seq scored 0.36), indicating that the clusters from Salus-STS data showed high similarity in transcriptomic profiles to those from scRNA-seq data. Notably, rare populations including myoid cells, macrophages and endothelial cells showed precise mapping to their scRNA-seq counterparts. This highlights the capability of Salus-STS to identify rare cell types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we mapped spatial distribution of major\u0026nbsp;cell types in\u0026nbsp;Fig.2D and Fig. S5. Cell annotation was further validated by examining the expression patterns of marker genes across different clusters (Fig.\u0026nbsp;2E).\u003c/p\u003e\n\u003cp\u003eTo highlight the importance of high UMI captures, we did a hypothetical experiment for testis tissue with different UMI levels. We subsampled the gene expression matrix to various UMI levels ranging from 14187 to 709 UMIs per 10 x 10 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e bin, and computed cluster-specific differentially expressed genes (DEG, Supplementary Data 1). DEG numbers exhibited a strong positive correlation with UMI counts, demonstrating high UMI level is critical to unveil full transcriptomic landscapes. Furthermore, we compared cell-cell communication networks disclosed by Salus-STS and Slide seq (10 \u0026mu;m resolution, 668 UMIs/bin 10 x 10 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e). Implementing CellChat\u003csup\u003e35\u003c/sup\u003e, Salus-STS identified 3242 intercellular communication networks, while Slide-seq identified only 1 of them (Fig. S4B, Supplementary data 2). We further downsampled Salus-STS data to a level of median ~700 UMIs per bin. No effective intercellular communication was identified, which is consistent with the Slide-Seq data. This paradigm effectively demonstrates how enhanced transcript capture efficiency fundamentally transforms spatial system biology analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther scrutinizing the data, we found some of our results are consistent with the literature and some are new.\u0026nbsp;For example,\u0026nbsp;it has been reported that\u0026nbsp;seminiferous tubules are constructed by tight junctions of Sertoli cells\u003csup\u003e36\u003c/sup\u003e, which are located in the outermost layer of the tubules, consistent with our results. Within these tubules, we observed the different\u0026nbsp;distributions of spermatogonia, spermatocytes, elongating spermatids, and round spermatids\u0026nbsp;in the tubules, which\u0026nbsp;align with our current understanding of mouse testis anatomy\u003csup\u003e37\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, our dataset identifies a broad range of somatic cells, including Leydig cells, endothelial cells, peritubular myoid cells, and macrophages\u0026nbsp;(Fig.2D and Fig. S5). Peritubular myoid cells, which surround the seminiferous tubules, are typically found to be a single layer in rodent testes\u003csup\u003e38\u003c/sup\u003e. Due to their rarity, these cells are often underrepresented in scRNA-seq data\u003csup\u003e27\u003c/sup\u003e. Remarkably, our dataset clearly reveals the structure of peritubular myoid cells, exhibiting a latticework pattern (Fig.2D and Fig. S5). Such a structure has not been reported in the literature using similar methods, demonstrating that the spatial resolution and capture efficiency are crucial in locating rare cells and revealing their spatial organizations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, in another area,\u0026nbsp;we found macrophages and Leydig cells colocalized (lower left corner of\u0026nbsp;Fig.2D and Fig. S5), together with enriched lymphocytes\u0026nbsp;while sperm cells were absent. This possibly suggests a\u0026nbsp;local inflammation incidence. We selected this region and a random control region from the testicular section and calculated the overall differential signaling pathways (Fig. S6A). GO enrichment analysis revealed that many of the top\u0026nbsp;ranked, upregulated\u0026nbsp;biological processes were linked to macrophage activation pathways (p.adjust \u0026lt; 0.001) and are related to major histocompatibility complex (MHC), indicating an upregulated antigen presenting activity of this area (Fig. S6B).\u0026nbsp;It has been reported that\u0026nbsp;macrophages and Leydig cells are\u0026nbsp;functionally\u0026nbsp;related and\u0026nbsp;macrophages may\u0026nbsp;produce cytokines\u0026nbsp;that tune the\u0026nbsp;steroidogenesis\u0026nbsp;of the\u0026nbsp;Leydig cell\u003csup\u003e39,40\u003c/sup\u003e. However, how this happens through a variety of signaling pathways is still largely unclear. High resolution, high capture efficiency spatial transcriptomics may suggest clues in solving these problems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of mouse testis architecture based on sub-cellular 2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2 \u0026mu;m\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;bins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the large number of genes and UMIs captured,\u0026nbsp;we analyzed the mouse testis dataset at 2 \u0026mu;m resolution. A ~0.7 \u0026times; 0.7 mm\u0026sup2; area\u0026nbsp;was selected, encompassing approximately seven seminiferous tubules. Using 2 \u0026times; 2 \u0026mu;m\u0026sup2; bins, we identified a median of 215 genes and 333 UMIs (Fig. 3A).\u003c/p\u003e\n\u003cp\u003eWe annotated each 2 \u0026times; 2 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e bin using RCTD method (Fig. 3B), revealing the seminiferous tubules and layered structures inside the tubules. The spatial distributions of single cell types revealed more details about the tissue structure (Fig. 3C), allowing us to observe islands of bins with sizes similar to a cell. For example, in Fig. 3C-iv, we see islands of bins which are likely individual macrophages. It has been reported that the transcriptome of Sertoli cells is closely associated with stages of seminiferous epithelial cycles\u003csup\u003e41,42\u003c/sup\u003e. Our spatial architecture demonstrates high resolution\u0026nbsp;Sertoli cell\u0026nbsp;distribution that different Sertoli cells from adjacent seminiferous tubules can be\u0026nbsp;well-separated\u0026nbsp;(Fig. 3C-vi). The high-resolution cell maps undoubtedly will\u0026nbsp;facilitate the study of Sertoli cell-germ cell interactions.\u003c/p\u003e\n\u003cp\u003eWe quantified the spatial distances between RCTD-annotated cell types and their marker genes (see Methods, Fig. 3D). The distance between elongating spermatids and their markers was well below 1 \u0026mu;m, indicating their colocalization. As a contrast, spermatocytes showed over10-fold greater distance to the elongating spermatid markers. We also profiled marker gene expression matrix of across cell types (Fig. 3E). These results together demonstrate convincing cell type recognition. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNote that RCTD, a deconvolution algorithm resolving cell compositions within \u0026quot;spots\u0026quot; containing multiple cells with mixed types, may not be ideal for the analysis of subcellular sized 2 \u0026mu;m bins. To avoid this problem, we tested to analyze the data using unsupervised clustering (Fig. S7A). The tissue architecture is similar to that disclosed by RCTD. Interestingly, expression profiles of clusters displayed a distinct pattern characterized by the absence of a highlighted \u0026ldquo;diagonal line\u0026rdquo; (Fig. S7B). This is possibly caused by intracellular heterogeneity: 2 \u0026times; 2 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e bins capture molecular signatures from distinct subcellular compartments rather than from whole cells. In fact, it is unclear to us so far how to best use the high resolution, high sensitivity data. However, current reasonable results and new structures disclosed suggest that subcellular level spatial transcriptomics is possible. It invites new ideas to dig out rich information buried in these data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatially resolved whole transcriptome of mouse brain\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo illustrate the \u003cem\u003ein situ\u003c/em\u003e capturing ability of Salus-STS, we analyzed an adult mouse hemibrain tissue section. The spatial heatmaps of UMIs and genes reveal the anatomical structure of the mouse brain section (Fig. 4A). Our dataset achieved a high detection sensitivity for mRNAs, with a median of 2,851 UMIs and 1,340 genes at a saturation of 0.46 for 10 \u0026times; 10 \u0026mu;m\u0026sup2; bins (Fig. 4B), higher than previous reports\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e. For examples, Open-ST captured 246 UMIs and 166 genes per 10 \u0026times; 10 \u0026mu;m\u0026sup2; bins at a saturation level \u0026gt; 0.9 (calculated from the published dataset). Nova-ST reported 349 UMIs and 199 genes for 10 \u0026times; 10 \u0026mu;m\u0026sup2; bins (no saturation reported) for adult mouse brain. For StereoSeq, the original manuscript did not report these values; however, multiple researchers calculated from the published dataset that the UMIs for StereoSeq are between 300~500 per 10 \u0026times; 10 \u0026mu;m\u0026sup2; bin at a saturation of \u0026gt; 0.9. We confirmed above calculations and concluded that current sequencing-based methods usually detect UMIs in lower hundreds per10 \u0026times; 10 \u0026mu;m\u0026sup2; bin for adult mouse brain, which is lower than that of Salus-STS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo showcase the high-definition transcriptome map disclosed by the data, we visualized the spatial distributions of specific marker genes in the hippocampus and cortical areas (Fig. 4C): \u003cem\u003eP\u003c/em\u003e\u003cem\u003eantr1,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eWfs1\u003c/em\u003e for CA1,\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eRgs14\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Sv2b\u0026nbsp;\u003c/em\u003efor CA2, \u003cem\u003eTspan18\u003c/em\u003e and \u003cem\u003eIl16\u003c/em\u003e for CA3, \u003cem\u003eProx1\u003c/em\u003e and \u003cem\u003eStxbp6\u003c/em\u003e for DG. Additionally, a series of cortical markers exhibit layer-specific distribution patterns (Fig. 4D). Impressively, the distribution of \u003cem\u003eCcn2\u003c/em\u003e clearly reveals the structure of L6b sublayer. Overall, we observed a consistent correspondence between the marker distributions and their respective anatomical regions, closely resembling the ISH data from the Allen Brain Atlas\u003csup\u003e43\u003c/sup\u003e.\u0026nbsp;To conclude, using 10\u0026nbsp;\u0026times;\u0026nbsp;10 \u0026mu;m\u0026sup2; bins as the elements of analysis and RCTD annotations with a scRNA-seq dataset as the reference\u003csup\u003e28\u003c/sup\u003e, we precisely reconstructed the cellular map of a mouse brain section, identifying 29 subclasses of glutamatergic neurons (Fig. S8), 6 sub-classes of GABAergic neurons (Fig. S9A), and 6 subclasses of non-neuronal cells (Fig. S9B). 41 out of 42 subclasses in the reference dataset were successfully characterized, demonstrating the ability of Salus-STS to identify major and rare cell sub-types.\u003c/p\u003e\n\u003cp\u003eThe mouse cortical area comprises diverse neurons and non-neuronal cells of various types and sizes. To better visualize the cellular landscape, we selected an area from the cortex and performed cell segmentations (i.e., cellbins, see Next section). RCTD annotations successfully mapped 14 distinct cell types, including 7 glutamatergic neuron subtypes (Car3, L2/3, L4, L4/5, L5, L5/6,\u0026nbsp;L6), which shows layer-specific distributions, 4 GABAergic neuron subtypes (Lamp5, Pvalb, Sncg, SST), and 3 non-neuronal types (astrocytes, microglia, and oligodendrocytes) (Fig. 4E). Our\u0026nbsp;dataset clearly reveals the spatial relationships between neurons and non-neuronal cells, which\u0026nbsp;are crucial in cell-cell interactions and communications, such as juxtacrine and paracrine signaling.\u003c/p\u003e\n\u003cp\u003eWe conducted a comparative analysis of Salus-STS and Stereo-seq in the hippocampal region from mouse brain data. Through evaluating a series of markers, Salus-STS demonstrated superior performance in both the proportion of marker-positive cells and the quantitative expression of these markers, indicated enhanced sensitivity (Fig. S10A). Furthermore, we performed integrated analysis combining scRNA-seq data with spatial data from both platforms (Fig. S10B). UMAP visualization reveals strong concordance in distribution patterns for major cell types such as Glutamatergic DG, CA3 and CA1. Notably, Salus-STS exhibited improved resolution for rare cell populations, e.g., non-neuronal endothelial cells and astrocytes, achieving clear cluster separation. In contrast, Stereo-seq data showed substantial overlap between these cell types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse brain analysis using Salus\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cellbins\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. 5 shows another example of mouse brains, whose\u0026nbsp;anatomic structure includes the cortex, hippocampus and thalamus, can be accurately identified using unsupervised clustering of 25 \u0026times; 25 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e bins (Fig. 5A). In this case, we analyzed the hemibrain using our cell segmentation algorithm (Salus Cellbins), which recognizes cell nuclei based on unspliced mRNA distribution and employs a watershed algorithm to define cell boundaries (Fig. 5B). The cell boundaries were highly consistent with the H\u0026amp;E staining images (Fig. S11A). We further examined the cell segmentation results by selecting the mouse hippocampal region, implementing both H\u0026amp;E imaging-based and unspliced mRNA-based methods. Both methods showed excellent cell type-marker gene correlation, validated by their gene expression matrixes (Fig. S11B, C).\u003c/p\u003e\n\u003cp\u003eNotably, cell segmentation improved clustering quality\u0026nbsp;in the hippocampus area by clearly distinguishing different regions as\u0026nbsp;compared to the original 25 \u0026times; 25 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e bin result (Fig. 5C, D). It has been reported CA2 area resembles a terminal portion of CA3 region\u003csup\u003e44\u003c/sup\u003e, thus is often recognized\u0026nbsp;as a part of CA3. However, CA2 exhibits distinct molecular and functional properties, as well as unique connectivity patterns that may be relevant to disease mechanisms\u003csup\u003e45\u003c/sup\u003e. Leveraging cell segmentation and annotation, Salus-STS successfully resolved the hippocampal subdivisions, including all\u0026nbsp;DG, CA1, CA2, and CA3 (Fig. 5D)\u0026nbsp;regions.\u003c/p\u003e\n\u003cp\u003eFurthermore, annotations of segmented cells by RCTD\u003csup\u003e46\u003c/sup\u003e revealed a high-definition cellular landscape in the mouse hippocampus, including clearly separated cortical layers (Fig. 5E). The cell annotation was rigorously verified by comparing the expression patterns of known cell markers across different cell types (Fig. 5F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLateral molecular diffusion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial accuracy of mRNA capture is a critical aspect of spatial transcriptomic methods, typically quantified by the lateral molecular diffusion of mRNAs with known expression patterns. To evaluate this, we\u0026nbsp;profiled the spatial transcriptome of mouse olfactory bulb (MOB). Unsupervised clustering with UMAP\u0026nbsp;at a bin size\u0026nbsp;of 10 \u0026times;\u0026nbsp;10 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e was performed. Clusters were annotated based on expression profiles of specific markers, which aligns well with tissue anatomy from the Allen Brain Atlas\u003csup\u003e43\u003c/sup\u003e (Fig. 6A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate molecular diffusion, we selected three neuronal cell-type-specific marker genes (Calb2, Cdhr1, and Slc17a7), which form distinct stripes near the mitral cell layer (MCL)\u003csup\u003e47\u003c/sup\u003e and examined their spatial distributions for different ST methods. We focused on the concentration level of each gene by selecting a 4-mm wide region where the MCL located in the middle (Fig. 6B, C), then calculated the mean concentration level at every 10 \u0026mu;m interval (Fig. 6D), hypothesizing that good control of RNA diffusion would produce a sharp peak in the spatial profile. For Salus-STS and Stereo-seq, all 3 genes exhibited sharp peaks near the MCL. In contrast, Pixel-seq and Slide-seq data showed less pronounced peaks, possibly suggesting low capture numbers and greater RNA dispersion caused by diffusion. The diffusion was also accessed by using mouse hippocampal markers (Fig. 6E). Lct, Prox1, and Stxbp6 were selected because of their specific expressions in the DG\u003csup\u003e48\u003c/sup\u003e region. Salus-STS showed superior control of diffusion, characterized by sharp peaks in the spatial profile among three ST methods (Fig. 6F).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSpatial transcriptomic methods have emerged as a powerful tool in biomedical research\u003csup\u003e49\u0026ndash;51\u003c/sup\u003e. Despite of\u0026nbsp;the technological advancements over the past decade, there still lacks a\u0026nbsp;method that offers simultaneous\u0026nbsp;high resolution, sensitive gene detection, and whole transcriptomic profiling. Some recent sequencing-based methods give a spatial resolution for capture addresses between 0.5 ~ 2 \u0026mu;m, well below the size of a biological cell. However, due to poor detection efficiency, the data have to be binned to have a size similar to or larger than a cell for further analysis. The advantage of subcellular address resolution cannot be fully exploited. Even after binning, the total number of genes and UMIs that are captured in a voxel equivalent in size to a cell are ~one order of magnitude smaller than those from single cell RNA sequencing. This introduces inaccuracy and uncertainty in subsequent data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we introduce Salus-STS, a sequencing-based method that relies on\u0026nbsp;solid-phase capture\u0026nbsp;arrays. Salus-STS has\u0026nbsp;three key features: (1) high spatial resolution for capture addresses of ~1 \u0026mu;m; (2) ultra-high probe\u0026nbsp;density\u0026nbsp;of ~55,000 +/- 10,000 probes/\u0026mu;m\u003csup\u003e2\u003c/sup\u003e,\u0026nbsp;enabling efficient whole transcriptome capture and\u0026nbsp;characterization; (3) flexible tuning of the\u0026nbsp;molecular recognition portions of the capture probes, providing versatility for studying\u0026nbsp;different types of targets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn studying adult\u0026nbsp;mouse testis and\u0026nbsp;brain, Salus-STS demonstrated the\u0026nbsp;highest sensitivity\u0026nbsp;in the literature to the best of our knowledge. It captured\u0026nbsp;a median of 13,834 UMIs and 4,199 genes per 10\u0026nbsp;\u0026times;\u0026nbsp;10 \u0026mu;m\u0026sup2; bin at a saturation of 0.43\u0026nbsp;for mouse testis, and\u0026nbsp;a median of 2,851 UMIs and 1,340 genes at a saturation of 0.46 for adult mouse brain. These numbers are much higher than literature reports\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e,\u0026nbsp;making Salus-STS approaching\u0026nbsp;scRNA-seq\u0026nbsp;in detection sensitivity.\u003c/p\u003e\n\u003cp\u003eThe high detection efficiency allowed us to analyze gene expression profiles for mouse testis with 2\u0026nbsp;\u0026times;\u0026nbsp;2 \u0026mu;m\u003csup\u003e2\u003c/sup\u003e bins, elements much smaller than a biological cell. Reasonable and new results about tissue architecture were obtained, which demonstrates that Salus-STS has great potential pushing sequencing-based spatial transcriptomics toward real sub-cellular resolutions\u003csup\u003e52\u003c/sup\u003e. In summary, we envision Salus-STS as a powerful tool that provides both high spatial resolution and high throughput for advancing research in life sciences and translational medicines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour supplementary figures are in the Supplementary Materials. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCodes and Data availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTestis single cell RNA data at the NCBI under GEO accession number GSE112393. Mouse brain reference single cell data generated by Allen Institute for Brain Science are available at web portal (https://portal.brain-map.org/atlases-and-data/rnaseq). Salus-STS gene expression matrix are available at the Google Drive: (https://drive.google.com/drive/folders/10krNQShSm7E3bgoM__cqxBspR-YSqihr?usp=sharing). OpenST adult mouse hippocampus and E13 head are available at web portal (https://rajewsky-lab.github.io/openst/examples/getting_started/). Stereo-seq mouse brain at CNGBdb under experiment ID (CNX0422300). Stereo-seq mouse brain v1.3 and Stereo-seq mouse brain cellbin data download from STOmics (https://en.stomics.tech/col1241/index.html, https://en.stomics.tech/col1311/index.html). Slide-seq mouse testis data download from https://www.dropbox.com/s/ygzpj0d0oh67br0/Testis_Slideseq_Data.zip?dl=0. \u003c/p\u003e\n\u003cp\u003eA standard pipeline is available for converting fastq files to bin-segmented expression matrices. The download link for the standard pipline is https://github.com/xuzaoxu/SalusSTS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by Shenzhen Science and Technology Program \u0026ldquo;KJZD20230923114220041\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGW, EL, LZ, YB, and RL conceived the project. GW, EL, LZ, YB, DFu, RL, and ZX supervised the whole project and designed the experiments. RL, DFu, HZ, and DFeng designed the Salus-STS capture substrates and cassettes. XZ, YiC, JC, and XL performed the majority of the experiments. ZX and HX designed bioinformatic pipelines. HL, CL, and ZX analyzed the data. GL participated in designing functional oligos. QC optimized enzymes for the biochemical reactions. WC, YC, LC，SX, CZ, YL, HW, and TF customized the DNA sequencer for spatial transcriptomics studies. DC synthesized modified dNTPs materials for the biochemical reaction. RL, ZX, DFu and GW wrote the manuscript. All authors read and approved the manuscript. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are employees of Salus BioMed Inc. Ltd. Correspondence and requests for materials should be addressed to [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAngerer, L. M. \u0026amp; Angerer, R. C. \u003cem\u003eDetection of Poly A + RNA in Sea Urchin Eggs and Embryos by Quantitative in Situ Hybridization\u003c/em\u003e. \u003cem\u003eNucleic Acids Research\u003c/em\u003e vol. 9 https://academic.oup.com/nar/article/9/12/2819/2380280 (1219).\u003c/li\u003e\n\u003cli\u003eRaj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. \u0026amp; Tyagi, S. 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Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Spatial transcriptomics, sequencing-based, spatial resolution, spermatogenesis, hippocampus, cortex","lastPublishedDoi":"10.21203/rs.3.rs-7337510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7337510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The advent of spatial transcriptomics has dramatically expanded our ability to study the vast network of cell-cell interactions at the molecular level in tissue. Among current methods, sequencing-based approaches have great potential in discovery because of their unbiased capture of the whole transcriptome. In the last couple of years, the spatial resolution for the capture addresses has been improved from ~100 μm to \u003c1 μm, well below the size of a mammalian cell. However, the capture efficiency of sequencing-based methods is generally low, which diminishes the effect of high capture resolution and negatively impacts subsequent data interpretation. Here, we introduce a new spatial transcriptomic system, which provides ~1 μm capture resolution and a much higher capture efficiency. We demonstrate that as high as ~14,000 UMIs can be captured from mouse testis sample per 10 × 10 μm² area at a sequencing saturation of 0.43. Rare cells organized in single-cell-layers can be recovered when the data was analyzed at an effective resolution of 2 μm.","manuscriptTitle":"Sequencing-based Spatial Transcriptomics with High Sensitivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 09:17:56","doi":"10.21203/rs.3.rs-7337510/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"106f9f7e-c87b-44ce-b418-816dbe90a2f6","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54052866,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":54052867,"name":"Biological sciences/Biological techniques/Gene expression analysis/Microarray analysis"}],"tags":[],"updatedAt":"2026-02-16T15:42:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 09:17:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7337510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7337510","identity":"rs-7337510","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}