As a general rule, it is accepted that the cellular localization of a protein is biologically critical for its function (Hung and Link, 2011). However, the general importance of RNA localization within a cell, and how this localization varies in different biological situations remains poorly understood. Targeted studies have identified examples of genes whose RNA localization is critical to function, such as the enrichment of beta-actin (Actb) RNA to lamellipodia in motile chicken embryonic myoblasts (Lawrence and Singer, 1986). It was observed that approximately 80% of total actin mRNA localized to the lamellipodia, and specific disruption of localization, but not expression, of the mRNA, resulted in decreased cell motility (Kislauskis et al., 1994; Kislauskis et al., 1997). The same authors also identified so-called ‘zipcode’ sequences in the 3’ UTR of Actb which were necessary for proper RNA localization (Kislauskis et al., 1994). In a larger-scale study, it has been estimated that 70% of mRNAs are spatially localized in Drosophila embryogenesis (Lécuyer et al., 2007). Other well-known and recently identified examples of RNA subcellular localization with functional consequences include lipid droplets (Saka and Valdivia, 2012) and TIS11B protein granules (Ma and Mayr, 2018). In these case studies, RNA localization is cis-regulated by either alternative splicing or 3’ UTR usage.

While the vast majority of 3’ UTR isoform functions remain unknown and incompletely annotated, emerging evidence points to an abundance of cell-type specific regulation (Meyer et al., 2022) where the inclusion of different 3’ UTRs may even have opposite functions. Cd47, for example, expresses a long-isoform 3’ UTR that results in a peripherally localized protein product protecting against phagocytosis, but can also express a short-isoform 3’ UTR that results in a cytoplasmic protein product with the same amino-acid sequence that does not confer the same phagocytotic protection (Berkovits and Mayr, 2015). Control of RNA subcellular localization through RNA isoform choice may help pinpoint functions for alternative RNA isoforms and UTRs in eukaryotes.

Spatial transcriptomics has seen rapidly increasing interest as methods become increasingly powerful and affordable (Marx, 2021). However, work remains primarily focused on gene expression. Techniques such as MERFISH (Moffitt et al., 2016), and its commercialization Vizgen, as well as SeqFISH+ (Eng et al., 2019) utilize sequential multiplexed fluorescence imaging to localize hundreds to thousands of distinct genes across a tissue with subcellular resolution. Along with RNA-capture-based spatial transcriptomics techniques (Ståhl et al., 2016; Stickels et al., 2021; Su et al., 2021), these spatial datasets have primarily been used to analyze the distribution of cell-types within a tissue via gene expression. At a finer scale, RNA distribution within cells has been understudied despite an established history of biologically important case studies discussed in multiple reviews (Lipshitz and Smibert, 2000; Holt and Bullock, 2009; Suter, 2018).

The limited approaches that have been used to detect subcellular localization patterns from high throughput, high-resolution spatial datasets rely on co-stains and/or heuristics without statistical formalism (Samacoits et al., 2018; Xue et al., 2020; Tang et al., 2021). As an example, an analysis of a SeqFISH + dataset relied on arbitrarily chosen hard thresholds to determine peripherally and centrally localizing genes in different mouse cortex cell-types. The use of thresholding can result in overlooked weaker spatial patterns and also makes it difficult to control the false discovery rate (FDR) (Eng et al., 2019). Additionally, compartment-based analysis of MERFISH datasets has been used to detect differences in neuron soma, axon, and dendrite transcriptomes using the Wilcoxon rank-sum test and Moran’s I (Moran, 1950; Xia et al., 2019). Discretizing cellular regions does not fully utilize the information present in the MERFISH dataset since RNA subcellular localization is intrinsically a continuous process. Similarly, while proximity-tagging and sequencing approaches such as APEX-seq (Fazal et al., 2019; Padrón and Ingolia, 2022) have generated high-plex datasets for RNA localization within subcellular compartments, these methods require genetically modified cell-lines, and cannot be readily applied to tissue. Finally, to our knowledge, no study has attempted to test whether isoform regulation can explain subcellular localization at the gene level in massively multiplexed FISH datasets.

To address the limitations of prior approaches, we introduce Subcellular Patterning Ranked Analysis With Labels (SPRAWL) as a transparent and statistical approach to detect RNA subcellular patterning from multiplexed imaging datasets. SPRAWL assigns an interpretable score to detect RNA localization patterning for a gene of interest in an individual cell. Furthermore, these scores can be carefully aggregated to detect spatial patterns between cell-types and biological replicates with FDR control. SPRAWL currently identifies continuous peripheral, central, radial, and punctate localization patterns which are significantly more extreme than expected by chance in either direction of effect. SPRAWL can be extended to detect user-defined patterns and represents a general framework for unbiased discovery of RNA subcellular localization patterns from multiplexed imaging datasets. This integrative approach identifies genes with potential cis-regulatory spatial sequences, and prioritizes candidates for experimental follow-ups.

SPRAWL was developed to be a non-parametric single-cell resolved measure of RNA subcellular localization that is robust against confounding variables of cell size, and RNA expression level, while providing effect-size and statistical significance measures. SPRAWL reduces complex spatial patterns into one-dimensional scores that are readily interpretable and comparable. An additional benefit of SPRAWL scores is their direct integration with other statistical methods: scores can be analyzed through the lens of various metadata such as cell type, or correlated with other measures such as RNA 3’ UTR regulation or splicing state.

SPRAWL is a publicly available Python package that can be installed using pypi with pip install subcellular-sprawl and has also been implemented in Nextflow (Di Tommaso et al., 2017) and Docker for reproducible analyses at large scale in high-performance or cloud computing environments. SPRAWL source code and documentation are available on GitHub, (copy archived at Bierman, 2024).

SPRAWL quantifies peripheral and central subcellular RNA patterning with rank statistics

Examples of RNA localized to the plasma membrane include Actin and Tubulin in mammalian cells (Lawrence and Singer, 1986), ASH1 in yeast (Bertrand et al., 1998), and Oskar in fly oocytes (Rongo et al., 1995). These foundational examples motivate the unbiased statistical detection of RNA localization patterns in reference to the cell-boundary. To satisfy this need, we’ve created the SPRAWL peripheral metric (Figure 1) which quantifies the extent to which the RNA spots of a gene of interest are more extremely proximal or distal from the cell-membrane than expected by chance.

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To calculate the SPRAWL peripheral metric for a given gene in a given cell, first, the minimum euclidean distance is calculated between each RNA spot, regardless of gene identity, and the cell-boundary. These distances are then used to rank the spots from 1 to n corresponding to the nearest and furthest RNA spot from the boundary, respectively (Figure 1a). The median rank is calculated for the m RNA spots of the gene. Under the null hypothesis that the gene is not peripherally localized, the expected value is (n+1)/2. Genes with lower median ranks than the expected value are more peripherally localizing, while larger median ranks correspond with anti-peripheral localization.

The probability mass function (PMF) of observing each possible median peripheral rank has a direct formulation which allows for exact calculations of p-values under the null (Figure 1b). The actual SPRAWL peripheral score, X, is the result of normalizing the median rank to be between –1 (anti-peripheral) and 1 (peripheral) with an expected value of 0 (not peripheral) (Figure 1c). Finally, the per cell-type scores can be calculated as the mean of the SPRAWL cell scores to provide an aggregate measure, Y, of RNA localization per gene per cell-type. Under the Lyapunov Central Limit Theorem (Billingsley, 1995), Y will approach in distribution a standard normal as the number of cells increases. The SPRAWL centrality score is conceptually identical to the peripheral score, but RNA spots are ranked by distance from the cell-centroid rather than the cell boundary. All subsequent steps are the same as the peripheral metric.

One of the main advantages of using a rank-based formulation of the periphery and centrality scores is the insensitivity to cell size and rotation. This feature facilitates direct comparisons of SPRAWL scores between cells and even samples. The simplicity of the statistically-backed metrics provides both effect size and p-value handles for detecting extreme RNA patterning in either the positive (peripheral/central) or negative (anti-peripheral/anti-central) direction of effect. Finally, it is worth noting that while the peripheral and central scores are strongly anti-correlated (Figure 1—figure supplement 1c), there are clear examples of RNA with simultaneously central and peripheral localization in a cell when the cell-boundary runs near to the cell centroid.

SPRAWL detection of punctate and radial patterning relies on gene-label permutations

While some RNAs are known to be peripherally or centrally localizing as discussed above, others are known to be trafficked to organelles (Chang et al., 2004), cell-poles (Rongo et al., 1995; Hachet and Ephrussi, 2004), or neuronal processes (Minis et al., 2014; Zappulo et al., 2017; Das et al., 2019). In all cases, RNA molecules of the same gene will be more spatially aggregated than expected by chance. To detect such patterning, SPRAWL punctate and radial metrics have been defined to respectively identify RNA species that tend to aggregate by euclidean distance or in one angular sector of the cell.

SPRAWL’s punctate score represents the degree to which RNA spots from a given gene are clustered together, scores closer to 1 indicate self-colocalizing or self-aggregating genes. Scores near –1 indicate self-repulsion, and scores of 0 indicate an expected level of aggregation under the null of random patterning.

When calculating the punctate score for a gene of interest with m>1 RNA spots in a cell, a subset of k random pairs of spots are selected and the distances between them are measured and averaged (Figure 2a). Next gene-label permutations are performed, randomly swapping gene labels but not RNA spot locations, to create a null background of mean between-spot distances by again choosing k random spots from the gene of interest in each permuted cell (Figure 2b). The punctate score, X, is normalized to be between –1 and 1 with E[X]=0 under the null (Figure 2c). Negative values indicate anti-punctate patterning, values near 0 are random or non-punctate, and positive values indicate punctate behavior (Figure 2d). Finally, SPRAWL cell-type scores can be calculated using the Lyapunov Central Limit theorem in the same manner as in the peripheral score (Figure 1d). The radial metric is conceptually identical to the punctate metric but measures mean between-spot angles instead of between-spot distances.

Figure 2

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Unlike the peripheral and central metrics, the radial and punctate scores rely on permutation testing to create a null distribution for each gene in each cell. The advantages of permutation testing are that the metrics can be of any complexity, but the disadvantage is the increased compute time in comparison with the simpler rank-based approaches. The permutation-based metrics retain the critical insensitivity to cell size, shape, and orientation present in the rank-based metrics.

SPRAWL detects cell-type specific localization patterns across biological replicates

We applied SPRAWL to the BICCN motor cortex (MOp) (Zhang et al., 2021), Vizgen Brainmap, and Vizgen Liver datasets (Vizgen, 2024) which each contained either biological or technical replicates. The median SPRAWL gene/cell-type scores were significantly positively correlated between replicates within all three datasets for all four spatial metrics having significant Pearson correlation with coefficients larger than 0.47 at an alpha level of 0.05 (Figure 3a: blue).

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Given the observed high pervasiveness of subcellular patterning in all datasets, we tested the specificity of SPRAWL by using permuted data. By permuting the gene-label of the RNA spots in a cell, we create negative control datasets that are known not to have significant spatial patterning. Assuringly, SPRAWL median gene/cell-type scores were not significantly correlated between biological replicates in any permuted dataset (Figure 3a: orange). Furthermore, in these negative control datasets, SPRAWL does not call any gene to be significantly localized in any cell-type after correcting for multiple hypothesis testing.

As an additional control for SPRAWL specificity, MERFISH and Vizgen experiments include ‘blank-codes’ which do not correspond to actual genes and are, therefore, not expected to have significant spatial patterning. In the BICCN MOp dataset, 10 blank-codes were included which SPRAWL determined to be spatially regulated in only the radial and punctate metrics. For the punctate metric, 191 of the 248 unique genes that had statistically significant patterning in at least one cell-type had smaller BH-corrected p-values than the most significant blank-codes. Similarly, for the radial metric, 232 of the 241 unique significant genes had a smaller p-value than the most significant blank-codes. SPRAWL did not identify significant patterning of blank-codes in any cell-type pairings across all replicates for the Vizgen Brainmap and Vizgen Liver datasets. Therefore, adjusting the p-value thresholds to filter out blank-codes would result in the loss of only 57 punctate and nine radial gene significance calls from only one dataset, again supporting SPRAWL’s specificity.

To test whether the SPRAWL peripheral score was sensitive to cell-segmentation, we compared SPRAWL before and after mutating the cell boundaries of a dataset (Methods and Figure 1—figure supplement 1e). Specifically, the cell boundary locations were computationally shrunk by a factor of 1.25 fold in the x and y direction, discarding spots that fell outside the new boundaries. In both the BICCN MOp and Vizgen Brainmap datasets, a Pearson correlation coefficient of greater than 0.85 was observed between the shrunk and original median gene/cell-type periphery scores. Insensitivity to cell-segmentation is an important feature of a subcellular localization algorithm due to the multitude of approaches and noted difficulties in computational cell segmentation (Coelho et al., 2009; Thomas and John, 2017; Vicar et al., 2019; Durkee et al., 2021).

While SPRAWL’s specificity can be benchmarked with multiple approaches, estimating SPRAWL’s sensitivity on real datasets is confounded by a lack of known true positive subcellular RNA patterning by cell-type. As a proxy for ground-truth, we hypothesized that RNAs encoding proteins with a signal recognition particle (SRP+) would have more centralized patterning than RNAs without (SRP-) due to their known trafficking to the endoplasmic reticulum. Surprisingly, the scores of all SPRAWL metrics were indistinguishably distributed between SRP+ and SRP- genes (Figure 5—figure supplement 1a). In an additional approach, we tested whether highly central RNAs were enriched in single-nucleus sequencing (snRNA-seq), compared to scRNA-seq, which was true for only a subset of genes (Figure 5—figure supplement 1b). A potential reason for both ground-truth proxies behaving unexpectedly is the nucleus is not necessarily centrally localized and RNAs may not be detectable when protein-bound.

Subcellular RNA localization is enriched for correlations with 3’ UTR length

Alternative 3’ UTRs and splice isoforms are known to result in differential mRNA localization (Kislauskis et al., 1994). Inclusion or exclusion of specific sequence elements can disrupt RNA binding proteins (RBPs) from binding and localizing the transcript. RBPs that have been identified as controlling transcript localization can have cell-type specific expression, including at the isoform level (Yisraeli, 2005; Müller-McNicoll and Neugebauer, 2013; Hentze et al., 2018). Examples of such RBPs include members of the RNA-transport granule (Kanai et al., 2004), providing a model for why RNAs may be cell type specifically localized as a function of their isoform. Conversely, differential localization of the same isoform can occur if the trans-acting localization factor is differentially expressed in different cell-types.

We coupled a recent statistical method to measure 3’ UTR length called the ReadZS (Meyer et al., 2021) with SPRAWL to identify genes with spatial localization correlated with 3’ UTR regulation (Booeshaghi et al., 2021). We used ReadZS to statistically quantify 3’ UTR lengths at single-cell resolution, and then computed the median ReadZS score by cell-type and gene on cell-type-matched 10Xv3 scRNA-seq datasets from the BICCN consortium (BRAIN Initiative Cell Census Network (BICCN), 2021). Spatial localization SPRAWL scores and ReadZS 3’ UTR lengths were correlated by gene/cell-type (Figure 4a). Twenty-six genes were detected as having significant SPRAWL/ReadZS correlation after BH multiple hypothesis correction at an FDR level of 0.05, a twofold enrichment compared to what is expected by chance (see Methods: Correlation analysis between SPRAWL and ReadZS). No significant gene/metric pairs were detected from the CZB mouse kidney/liver dataset which was the only other dataset with matched scRNA-seq. The lack of significant correlations between the SPRAWL metric score and 3’ UTR length in this dataset could be due to multiple factors, including this dataset having fewer coarser cell-type definitions.

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Timp3 3’ UTR length predicts peripheral localization

In the BICCN data, Timp3 has the largest observed variation in estimated 3’ UTR length between cell-types, with the most divergent read-buildup between layer-6 corticothalamic (L6 CT) and endothelial cells reflecting at least two dominant 3’ UTRs differing in length by >2 kilobases (Figure 5a). These 3’ UTR read densities were consistent across mouse biological sequencing replicates within 10 X scRNA-seq experiments. Only one UTR is annotated, though a gene antisense to Timp3, Sync3 on the minus strand, overlaps its transcriptional radius. We are confident that observed reads can be confidently attributed to Timp3 as Sync3’s nearest exon is ~5 kb from Timp3’s UTR and plus-strand mapping reads alone were analyzed.

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Timp3 is a secreted matrix metalloprotease inhibitor that has been implicated in multiple diseases ranging from cardiomyopathies to macular dystrophies (Weber et al., 1994; Schrimpf et al., 2012), but subcellular RNA localization patterns have not been reported. Elevated Timp3 gene expression (Capone et al., 2016) is linked to compromised cerebral blood flow, and the RNA is experimentally validated to be a target of microRNA (miRNA) regulation (Fiorentino et al., 2013). We observe Timp3 RNA to be significantly peripheral in L6 IT neurons; while being insignificantly peripherally localized in Sst cells. SPRAWL and ReadZS 3’ UTR scores had a significant negative correlation of R=−0.68 and p<<0.05 Pearson BH-corrected p-value. Timp3’s longer, annotated 3’ UTR isoform is expressed in cell-types with significantly less peripheral localization as compared to shorter unannotated isoforms (Figure 5b).

We studied whether Timp3’s 3’ UTR length was more globally regulated in endothelial and other cell-types through scRNA-seq and in different biological contexts in both mouse and human datasets and extended the analysis to include Timp2. Mouse and human Timp3 have a 96.2% amino acid sequence similarity with mouse and human Timp2 having an even higher 98.2% sequence identity. ReadZS also detected statistically significant Timp3 3’ UTR length shifts between cell-types from the Tabula Sapiens consortium Jones et al., 2022 in the lung and other tissues (Figure 4—figure supplement 1a). Furthermore, we found both Timp2 and Timp3 UTR lengths to be regulated in lung tissue slices across endothelial, epithelial, immune, and stroma cell-type compartments (Figure 5c and d). Since SPRAWL identified a highly negative correlation between Timp3 peripheral subcellular localization and 3’ UTR length, and since Timp3 3’ UTRs become shorter during lung culture, the subcellular localization of Timp3 is predicted to shift to a more peripheral distribution during the lung culture. In conjunction with 3’ UTR length shortening, gene expression of Timp3 decreases over this time course in all cell-type groups (Figure 4—figure supplement 1b).

Both mouse and human Timp3 show high conservation within its 3’ UTR. Conservation is particularly high near the two dominant alternative 3’ UTR regions (Figure 5a and c: Cons 100 Verts track), all but one of which are un-annotated. These regions could contain alternative end processing or regulatory sequences. In the case of mouse Timp3, this includes annotated binding sites for miR-181c-5p and miR-221–3 p and RBPs Cirbp, Cpsf6, and Celf1 (Figure 5a). The 3’ UTR isoforms differentially include these regions, releasing the shorter isoforms from regulatory pressures by more distal elements, including the experimentally validated miR-21 that binds in the 3’ UTR of human Timp3 (Hu et al., 2016). In this study, the authors found that high expression of miR-21 led to repression of Timp3 and pathogenic activation of angiogenesis.

Together, we hypothesize that Timp3 may have both secreted and non-secreted isoforms, with a precedent set by Cd47 (Berkovits and Mayr, 2015). Furthermore, we hypothesize that this regulation is controlled by alternative 3’ UTR isoform lengths that determine subcellular RNA localization through interaction with RBPs and microRNAs that specifically bind the longer isoform. This example illustrates the power of SPRAWL for unsupervised discovery of subcellular localization and its integration with isoform-resolved, annotation-free analysis of scRNA-seq to generate testable biological hypotheses regarding isoform-specific regulation and function.

Human brain pericyte cell culture shows differential temporal Timp3 3’ UTR usage

Motivated by the findings that (1) mouse brain cell-types expressing shorter Timp3 3’ UTR isoforms were correlated with increasingly peripherally localized Timp3 RNA (Figure 5b), and (2) Timp3 3’ UTR lengths decrease throughout human lung slice culture (Figure 5c and d), we hypothesized that Timp3 protein secretion would be sensitive to RNA localization and/or 3’ UTR length. We tested this hypothesis using a human brain pericyte cell-line known to express Timp3 protein. The pericytes were cultured over 5 d with supernatant samples collected at 6, 24, 48, and 72 after plating. At each timepoint, the number of cells, total extracellular protein concentration (BCA), extracellular Timp3 protein (ELISA), and Timp3 RNA (qPCR) were measured (Figure 6a).

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We observed that the rate of per-cell Timp3 protein secretion, as measured by ELISA, does not significantly change throughout culture time, averaging 350 Timp3 protein molecules per-cell per-hour. The approximately 15- hr half-life of Timp3 protein in cell culture (Mao et al., 2021) was taken into account when making these calculations (Methods Timp3 protein production estimation). However, total extracellular protein per-cell slightly decreased from 6 to 24 hr of cell culture as measured by BCA (Figure 6b). Taken together these findings suggest that Timp3 protein production is not variable during cell culture.

From the previous human lung culture experiment (Figure 5c), we hypothesized that the abundance of shortened 3’ UTRs of Timp3 would increase relative to the canonical full-length isoform throughout pericyte cell culture. To test this hypothesis, Timp3 short and long 3’ UTR abundance were estimated using proximal and distal qPCR primers. The proximal qPCR primer pair is designed to amplify both full-length canonical and un-annotated shortened 3’ UTR Timp3 templates. The distal qPCR primer pair, however, can only amplify the full-length isoform (Figure 6c). In support of our hypothesis, we observe the ratio of Timp3 distal to proximal RNA abundance significantly decreased from 24 to 48 hr by a factor of 1.5 X (Figure 6d).

Additionally, Timp3 3’ UTR expression decreased by half between 6 and 24 hr before doubling between 48 and 72 hr as measured by both proximal and distal qPCR primers (Figure 6e). The large fluctuations in Timp3 expression relative to multiple house-keeping genes are noteworthy since the Timp3 protein production levels remained constant throughout the experiment. This observation may suggest post-transcriptional or post-translational regulation. In conclusion, transcripts of Timp3 with the proximal 3’ UTR region increased in relation to the distal region during pericyte culture, which is in agreement with our hypothesis from the human lung culture model.

Highly multiplexed spatial transcriptomics datasets are becoming increasingly available, but analysis tools have overwhelmingly focused on localizing cell-types within tissue, rather than RNA within cells. SPRAWL addresses this need as a novel non-parametric approach for unbiased detection of subcellular RNA localization patterns. In this study, SPRAWL provides evidence for (1) highly consistent RNA patterning across biological replicates, (2) abundant cell-type specific RNA localization, and (3) differential patterning dependent on 3’ UTR isoform.

We show that SPRAWL has perfect specificity when benchmarked on simulated negative control datasets, yet identifies thousands of significant genes with extreme RNA localization patterns by cell-type in real datasets. The simplicity of the SPRAWL score facilitates integration with other datasets and tools for follow-up computational studies. We’ve been able to illustrate this concept by leveraging existing scRNA-seq datasets and the ReadZS tool (Meyer et al., 2022) to find genes with correlated patterning and 3’ UTR usage. Additionally, SPRAWL results can motivate experimental studies that detect novel biology as we’ve shown by identifying shifting Timp3 3’ UTR isoform usage in a pericyte culture experiment.

SPRAWL prioritizes potentially functionally important isoform expression for further study such as Timp3, Slc32a1, Cxcl14, and Nxph1 which have significant spatial and 3’ UTR-usage correlation between cell-types. SPRAWL generates testable hypotheses of cis-regulatory elements that alter RNA localization which is of high interest because in mice and humans, more than 96% of genes are alternatively spliced and UTR regulation is pervasive but poorly annotated (Olivieri et al., 2021; Olivieri et al., 2022).

The localization scores generated by SPRAWL are versatile and can be computed for proteins rather than RNA. In fact, trans-regulated spatial events can be detected in future work by applying SPRAWL to subcellular protein localization datasets generated by tools such as CODEX (Black et al., 2021) or MIBI (Keren et al., 2019). Furthermore, the SPRAWL framework can be used to implement different measures of subcellular localization. Some but not all statistically significant patterns detected by SPRAWL are ‘striking to the human eye,’ which has implications for whether human-guided or statistical-guided inferences are preferred and which are more biologically meaningful.

The importance of the correlation between SPRAWL subcellular localization and isoform expression, including Timp3, Slc32a1, Cxcl14, and Nxph1, was minimally explored in this work. Still, we hypothesize a causal link between 3’ UTR regulation, localization, and potential protein function, as was observed for Actb, which could guide future experimental efforts, as well as help pinpoint cell-type specific functions. Our in vitro human pericyte cell culture experiment, for example, showed that pericytes are utilizing a previously unknown shortened Timp3 3’ UTR in addition to the full-length isoform. Furthermore, a shift towards more shortened 3’ UTR usage occurs during pericyte cell culture; a result that mirrors SPRAWL findings in human lung tissue.

Sampling a handful of tissues and cell types, SPRAWL identified tens of RNA species with subcellular localization related to cell type. Many technical limitations suggest that this number is a significant underestimate: for one, MERFISH-based approaches require probes to be pre-specified, and thus they (a) aggregate isoforms, confounding cases where two co-expressed isoforms have dramatically different localization patterns; (b) miss isoforms that lack sequence contained in the probe set measurements. Furthermore, single-cell sequencing technology and analysis may be under-ascertaining RNA expression due to (i) sampling depth; (ii) poly-A capture bias, and (iii) a dearth of computational algorithms to analyze isoform-specific differences. Through the ReadZS we have collapsed UTR variation to a single scalar value (Meyer et al., 2021; Chaung et al., 2022; Olivieri et al., 2022) but we have not explored correlations with RNA splicing or other sequence variants, a topic of further research. Our findings support a model where 3’ UTR regulation at the nucleotide level controls localization through function. If this is true, imaging-based technology like MERFISH will have limited power over discovery and in situ sequencing may be a preferred approach. Together, this suggests that isoform-specific localization may be widespread and confer functions that should be tested in future computational and experimental work.

SPRAWL provides an estimate of the pervasiveness of cell-type and 3’ UTR-regulated RNA localization. Limitations of the study include possible confounding by technical artifacts from probe hybridization, improper cell-segmentation, and bias in the gene panel selected for imaging. Additionally, our decision not to use nuclei boundaries blinds us to situations where an RNA may be highly peripheral, but still within the cell nucleus. This could mean UTR peripherality is confounded with the dynamis of export, including transcription at the nuclear periphery. We have attempted to address these potential artifacts through hundreds of thousands of observations and by permutation where possible. Additionally, computationally shrinking cell-boundaries resulted in only minimal changes in SPRAWL scores. Future work on novel datasets using different segmentation approaches will provide further confidence that SPRAWL detects biologically relevant patterns. We believe the current implementation of SPRAWL is conservative and likely misses patterns due to optical crowding and low-abundance gene expression.

There exists no directly competing method to SPRAWL which is able to leverage highly multiplexed imaging datasets, requiring only RNA spot locations, cell-boundary estimates, and gene identity of each RNA spot. Many current software approaches aim to discretize RNA patterning into subcompartments and rely on co-stains which are not guaranteed to be present in every dataset. Other approaches use statistically opaque machine-learning-based classifiers to assign RNA spots to pre-specified patterns (Mah et al., 2022). As spatial transcriptomics methods are commercialized and become more accessible, increasing numbers of public datasets will become available and can be processed by SPRAWL regardless of the tissue or study design.

The current manuscript is mostly a computational study, but the Timp3 pericyte experiment did generate data which is available in the SPRAWL GitHub, (copy archived at Bierman, 2024).

1. 1. Company V

   (2022) Google Cloud

   ID vizgen-mouse-brain-map. Vizgen MERFISH Mouse Brain Map (BrainMap).

   https://console.cloud.google.com/marketplace/product/gcp-public-data-vizgen/vizgen-mouse-brain-map
2. 1. Company V

   (2022) Google Cloud

   ID 832a9f61-22d3-44c1-a2cf-838c166d9ac5. Vizgen MERFISH Liver showcase.

   https://info.vizgen.com/mouse-liver-data?submissionGuid=832a9f61-22d3-44c1-a2cf-838c166d9ac5
3. 1. Pisco A

   (2022) figshare

   ID 134213. MERFISH mouse comparison study.

   https://figshare.com/projects/MERFISH_mouse_comparison_study/134213
4. 1. CHL Eng
   2. Lawson M
   3. Zhu Q
   4. Dries R
   5. Koulena N
   6. Takei Y
   7. Yun J
   8. Cronin C
   9. Karp C
   10. Yuan GC
   11. Cai L

   (2019) GitHub

   ID ee6c416. SeqFish+ dataset.

   https://github.com/CaiGroup/seqFISH-PLUS/blob/master/sourcedata.zip?raw=true
5. 1. Zhang M
   2. Eichhorn SW
   3. Zingg B
   4. Yao Z
   5. Cotter K
   6. Zeng H
   7. Dong H
   8. Zhuang X

   (2021) Brain Image Library

   ID cf1c1a431ef8d021. MERFISH primary mouse cortex (MOp) dataset.

   https://download.brainimagelibrary.org/cf/1c/cf1c1a431ef8d021/processed_data/

1. 1. Banisadr G
   2. Bhattacharyya BJ
   3. Belmadani A
   4. Izen SC
   5. Ren D
   6. Tran PB
   7. Miller RJ

   (2011) The chemokine BRAK/CXCL14 regulates synaptic transmission in the adult mouse dentate gyrus stem cell niche

   Journal of Neurochemistry 119:1173–1182.

   https://doi.org/10.1111/j.1471-4159.2011.07509.x

   • PubMed
   • Google Scholar
2. 1. Bässler EL
   2. Ngo-Anh TJ
   3. Geisler HS
   4. Ruppersberg JP
   5. Gründer S

   (2001) Molecular and functional characterization of acid-sensing ion channel (ASIC) 1b

   The Journal of Biological Chemistry 276:33782–33787.

   https://doi.org/10.1074/jbc.M104030200

   • PubMed
   • Google Scholar
3. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society Series B 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
4. 1. Berkovits BD
   2. Mayr C

   (2015) Alternative 3’ UTRs act as scaffolds to regulate membrane protein localization

   Nature 522:363–367.

   https://doi.org/10.1038/nature14321

   • PubMed
   • Google Scholar
5. 1. Bertrand E
   2. Chartrand P
   3. Schaefer M
   4. Shenoy SM
   5. Singer RH
   6. Long RM

   (1998) Localization of ASH1 mRNA particles in living yeast

   Molecular Cell 2:437–445.

   https://doi.org/10.1016/s1097-2765(00)80143-4

   • PubMed
   • Google Scholar
6. Software

   1. Bierman R

   (2024) SPRAWL, version swh:1:rev:a25bee0b043ec49f456892b4ed9a361d284759d2

   Software Heritage.

   https://archive.softwareheritage.org/swh:1:dir:78e521b6400a36714099a7b662cdf14408675cba;origin=https://github.com/salzman-lab/SPRAWL;visit=swh:1:snp:6847a765bba7e6823e845666ffd73e3e50c5708d;anchor=swh:1:rev:a25bee0b043ec49f456892b4ed9a361d284759d2
7. 1. Billingsley P

   (1995)

   WIley series in probability and mathematical statistics

   Probability and measure, WIley series in probability and mathematical statistics, Wiley.

   • Google Scholar
8. 1. Black S
   2. Phillips D
   3. Hickey JW
   4. Kennedy-Darling J
   5. Venkataraaman VG
   6. Samusik N
   7. Goltsev Y
   8. Schürch CM
   9. Nolan GP

   (2021) CODEX multiplexed tissue imaging with DNA-conjugated antibodies

   Nature Protocols 16:3802–3835.

   https://doi.org/10.1038/s41596-021-00556-8

   • PubMed
   • Google Scholar
9. 1. Booeshaghi AS
   2. Yao Z
   3. van Velthoven C
   4. Smith K
   5. Tasic B
   6. Zeng H
   7. Pachter L

   (2021) Isoform cell-type specificity in the mouse primary motor cortex

   Nature 598:195–199.

   https://doi.org/10.1038/s41586-021-03969-3

   • PubMed
   • Google Scholar
10. 1. Born G
    2. Breuer D
    3. Wang S
    4. Rohlmann A
    5. Coulon P
    6. Vakili P
    7. Reissner C
    8. Kiefer F
    9. Heine M
    10. Pape H-C
    11. Missler M

    (2014) Modulation of synaptic function through the α-neurexin-specific ligand neurexophilin-1

    PNAS 111:E1274–E1283.

    https://doi.org/10.1073/pnas.1312112111

    • PubMed
    • Google Scholar
11. 1. BRAIN Initiative Cell Census Network (BICCN)

    (2021) A multimodal cell census and atlas of the mammalian primary motor cortex

    Nature 598:86–102.

    https://doi.org/10.1038/s41586-021-03950-0

    • PubMed
    • Google Scholar
12. Software

    1. Cai L
    2. Lawson M
    3. Shah S
    4. Pierson N

    (2019) SeqFISH-PLUS

    Software Heritage.

    https://github.com/CaiGroup/seqFISH-PLUS/
13. 1. Capone C
    2. Dabertrand F
    3. Baron-Menguy C
    4. Chalaris A
    5. Ghezali L
    6. Domenga-Denier V
    7. Schmidt S
    8. Huneau C
    9. Rose-John S
    10. Nelson MT
    11. Joutel A

    (2016) Mechanistic insights into a TIMP3-sensitive pathway constitutively engaged in the regulation of cerebral hemodynamics

    eLife 5:e17536.

    https://doi.org/10.7554/eLife.17536

    • PubMed
    • Google Scholar
14. 1. Chang P
    2. Torres J
    3. Lewis RA
    4. Mowry KL
    5. Houliston E
    6. King ML

    (2004) Localization of RNAs to the mitochondrial cloud in Xenopus oocytes through entrapment and association with endoplasmic reticulum

    Molecular Biology of the Cell 15:4669–4681.

    https://doi.org/10.1091/mbc.e04-03-0265

    • PubMed
    • Google Scholar
15. Preprint

    1. Chaung K
    2. Baharav T
    3. Zheludev I
    4. Salzman J

    (2022) A Statistical, Reference-Free Algorithm Subsumes Myriad Problems in Genome Science and Enables Novel Discovery

    bioRxiv.

    https://doi.org/10.1101/2022.06.24.497555

    • Google Scholar
16. 1. Coelho LP
    2. Shariff A
    3. Murphy RF

    (2009) Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms

    Proceedings. IEEE International Symposium on Biomedical Imaging 5193098:518–521.

    https://doi.org/10.1109/ISBI.2009.5193098

    • PubMed
    • Google Scholar
17. 1. Das S
    2. Singer RH
    3. Yoon YJ

    (2019) The travels of mRNAs in neurons: do they know where they are going?

    Current Opinion in Neurobiology 57:110–116.

    https://doi.org/10.1016/j.conb.2019.01.016

    • PubMed
    • Google Scholar
18. 1. Di Tommaso P
    2. Chatzou M
    3. Floden EW
    4. Barja PP
    5. Palumbo E
    6. Notredame C

    (2017) Nextflow enables reproducible computational workflows

    Nature Biotechnology 35:316–319.

    https://doi.org/10.1038/nbt.3820

    • PubMed
    • Google Scholar
19. 1. Durkee MS
    2. Abraham R
    3. Clark MR
    4. Giger ML

    (2021) Artificial intelligence and cellular segmentation in tissue microscopy images

    The American Journal of Pathology 191:1693–1701.

    https://doi.org/10.1016/j.ajpath.2021.05.022

    • PubMed
    • Google Scholar
20. 1. Ebihara S
    2. Obata K
    3. Yanagawa Y

    (2003) Mouse vesicular GABA transporter gene: genomic organization, transcriptional regulation and chromosomal localization

    Brain Research. Molecular Brain Research 110:126–139.

    https://doi.org/10.1016/s0169-328x(02)00648-4

    • PubMed
    • Google Scholar
21. Software

    1. Emanuel G
    2. Babcock H

    (2020) ZhuangLab/merlin: merlin, version 0.1.6

    GitHub.

    https://github.com/ZhuangLab/MERlin/blob/master/docs/installation.rst
22. 1. Eng C-HL
    2. Lawson M
    3. Zhu Q
    4. Dries R
    5. Koulena N
    6. Takei Y
    7. Yun J
    8. Cronin C
    9. Karp C
    10. Yuan G-C
    11. Cai L

    (2019) Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+

    Nature 568:235–239.

    https://doi.org/10.1038/s41586-019-1049-y

    • Google Scholar
23. 1. Fazal FM
    2. Han S
    3. Parker KR
    4. Kaewsapsak P
    5. Xu J
    6. Boettiger AN
    7. Chang HY
    8. Ting AY

    (2019) Atlas of subcellular RNA localization revealed by APEX-Seq

    Cell 178:473–490.

    https://doi.org/10.1016/j.cell.2019.05.027

    • PubMed
    • Google Scholar
24. 1. Fiorentino L
    2. Cavalera M
    3. Mavilio M
    4. Conserva F
    5. Menghini R
    6. Gesualdo L
    7. Federici M

    (2013) Regulation of TIMP3 in diabetic nephropathy: a role for microRNAs

    Acta Diabetologica 50:965–969.

    https://doi.org/10.1007/s00592-013-0492-8

    • PubMed
    • Google Scholar
25. 1. Gasnier B

    (2004) The SLC32 transporter, a key protein for the synaptic release of inhibitory amino acids

    Pflügers Archiv European Journal of Physiology 447:756–759.

    https://doi.org/10.1007/s00424-003-1091-2

    • Google Scholar
26. Software

    1. Gillies S
    2. van der Wel C
    3. Van den Bossche J
    4. Taves MW
    5. Arnott J
    6. Ward BC

    (2007) Shapely: manipulation and analysis of geometric objects

    GitHub.

    https://github.com/SpaceKnow/Shapely
27. 1. Hachet O
    2. Ephrussi A

    (2004) Splicing of oskar RNA in the nucleus is coupled to its cytoplasmic localization

    Nature 428:959–963.

    https://doi.org/10.1038/nature02521

    • PubMed
    • Google Scholar
28. 1. Hentze MW
    2. Castello A
    3. Schwarzl T
    4. Preiss T

    (2018) A brave new world of RNA-binding proteins

    Nature Reviews. Molecular Cell Biology 19:327–341.

    https://doi.org/10.1038/nrm.2017.130

    • PubMed
    • Google Scholar
29. 1. Holt CE
    2. Bullock SL

    (2009) Subcellular mRNA localization in animal cells and why it matters

    Science 326:1212–1216.

    https://doi.org/10.1126/science.1176488

    • PubMed
    • Google Scholar
30. 1. Hromas R
    2. Broxmeyer HE
    3. Kim C
    4. Nakshatri H
    5. Christopherson K
    6. Azam M
    7. Hou YH

    (1999) Cloning of BRAK, a novel divergent CXC chemokine preferentially expressed in normal versus malignant cells

    Biochemical and Biophysical Research Communications 255:703–706.

    https://doi.org/10.1006/bbrc.1999.0257

    • PubMed
    • Google Scholar
31. 1. Hu J
    2. Ni S
    3. Cao Y
    4. Zhang T
    5. Wu T
    6. Yin X
    7. Lang Y
    8. Lu H

    (2016) The angiogenic effect of microRNA-21 Targeting TIMP3 through the regulation of MMP2 and MMP9

    PLOS ONE 11:e0149537.

    https://doi.org/10.1371/journal.pone.0149537

    • Google Scholar
32. 1. Hung MC
    2. Link W

    (2011) Protein localization in disease and therapy

    Journal of Cell Science 124:3381–3392.

    https://doi.org/10.1242/jcs.089110

    • PubMed
    • Google Scholar
33. 1. Jin J
    2. Sison K
    3. Li C
    4. Tian R
    5. Wnuk M
    6. Sung H-K
    7. Jeansson M
    8. Zhang C
    9. Tucholska M
    10. Jones N
    11. Kerjaschki D
    12. Shibuya M
    13. Fantus IG
    14. Nagy A
    15. Gerber H-P
    16. Ferrara N
    17. Pawson T
    18. Quaggin SE

    (2012) Soluble FLT1 binds lipid microdomains in podocytes to control cell morphology and glomerular barrier function

    Cell 151:384–399.

    https://doi.org/10.1016/j.cell.2012.08.037

    • PubMed
    • Google Scholar
34. 1. Jones RC
    2. Karkanias J
    3. Krasnow MA
    4. Pisco AO
    5. Quake SR
    6. Salzman J
    7. Yosef N
    8. Bulthaup B
    9. Brown P
    10. Harper W
    11. Hemenez M
    12. Ponnusamy R
    13. Salehi A
    14. Sanagavarapu BA
    15. Spallino E
    16. Aaron KA
    17. Concepcion W
    18. Gardner JM
    19. Kelly B
    20. Neidlinger N
    21. Wang Z
    22. Crasta S
    23. Kolluru S
    24. Morri M
    25. Tan SY
    26. Travaglini KJ
    27. Xu C
    28. Alcántara-Hernández M
    29. Almanzar N
    30. Antony J
    31. Beyersdorf B
    32. Burhan D
    33. Calcuttawala K
    34. Carter MM
    35. Chan CKF
    36. Chang CA
    37. Chang S
    38. Colville A
    39. Culver RN
    40. Cvijović I
    41. D’Amato G
    42. Ezran C
    43. Galdos FX
    44. Gillich A
    45. Goodyer WR
    46. Hang Y
    47. Hayashi A
    48. Houshdaran S
    49. Huang X
    50. Irwin JC
    51. Jang S
    52. Juanico JV
    53. Kershner AM
    54. Kim S
    55. Kiss B
    56. Kong W
    57. Kumar ME
    58. Kuo AH
    59. Li B
    60. Loeb GB
    61. Lu W-J
    62. Mantri S
    63. Markovic M
    64. McAlpine PL
    65. de Morree A
    66. Mrouj K
    67. Mukherjee S
    68. Muser T
    69. Neuhöfer P
    70. Nguyen TD
    71. Perez K
    72. Puluca N
    73. Qi Z
    74. Rao P
    75. Raquer-McKay H
    76. Schaum N
    77. Scott B
    78. Seddighzadeh B
    79. Segal J
    80. Sen S
    81. Sikandar S
    82. Spencer SP
    83. Steffes LC
    84. Subramaniam VR
    85. Swarup A
    86. Swift M
    87. Van Treuren W
    88. Trimm E
    89. Veizades S
    90. Vijayakumar S
    91. Vo KC
    92. Vorperian SK
    93. Wang W
    94. Weinstein HNW
    95. Winkler J
    96. Wu TTH
    97. Xie J
    98. Yung AR
    99. Zhang Y
    100. Detweiler AM
    101. Mekonen H
    102. Neff NF
    103. Sit RV
    104. Tan M
    105. Yan J
    106. Bean GR
    107. Charu V
    108. Forgó E
    109. Martin BA
    110. Ozawa MG
    111. Silva O
    112. Toland A
    113. Vemuri VNP
    114. Afik S
    115. Awayan K
    116. Botvinnik OB
    117. Byrne A
    118. Chen M
    119. Dehghannasiri R
    120. Gayoso A
    121. Granados AA
    122. Li Q
    123. Mahmoudabadi G
    124. McGeever A
    125. Olivieri JE
    126. Park M
    127. Ravikumar N
    128. Stanley G
    129. Tan W
    130. Tarashansky AJ
    131. Vanheusden R
    132. Wang P
    133. Wang S
    134. Xing G
    135. Dethlefsen L
    136. Ezran C
    137. Gillich A
    138. Hang Y
    139. Ho P-Y
    140. Irwin JC
    141. Jang S
    142. Leylek R
    143. Liu S
    144. Maltzman JS
    145. Metzger RJ
    146. Phansalkar R
    147. Sasagawa K
    148. Sinha R
    149. Song H
    150. Swarup A
    151. Trimm E
    152. Veizades S
    153. Wang B
    154. Beachy PA
    155. Clarke MF
    156. Giudice LC
    157. Huang FW
    158. Huang KC
    159. Idoyaga J
    160. Kim SK
    161. Kuo CS
    162. Nguyen P
    163. Rando TA
    164. Red-Horse K
    165. Reiter J
    166. Relman DA
    167. Sonnenburg JL
    168. Wu A
    169. Wu SM
    170. Wyss-Coray T
    171. Tabula Sapiens Consortium

    (2022) The tabula sapiens: a multiple-organ, single-cell transcriptomic atlas of humans

    Science 376:eabl4896.

    https://doi.org/10.1126/science.abl4896

    • PubMed
    • Google Scholar
35. 1. Kanai Y
    2. Dohmae N
    3. Hirokawa N

    (2004) Kinesin transports RNA: isolation and characterization of an RNA-transporting granule

    Neuron 43:513–525.

    https://doi.org/10.1016/j.neuron.2004.07.022

    • Google Scholar
36. 1. Kang J
    2. Tang Q
    3. He J
    4. Li L
    5. Yang N
    6. Yu S
    7. Wang M
    8. Zhang Y
    9. Lin J
    10. Cui T
    11. Hu Y
    12. Tan P
    13. Cheng J
    14. Zheng H
    15. Wang D
    16. Su X
    17. Chen W
    18. Huang Y

    (2022) RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility

    Nucleic Acids Research 50:D326–D332.

    https://doi.org/10.1093/nar/gkab997

    • PubMed
    • Google Scholar
37. 1. Keren L
    2. Bosse M
    3. Thompson S
    4. Risom T
    5. Vijayaragavan K
    6. McCaffrey E
    7. Marquez D
    8. Angoshtari R
    9. Greenwald NF
    10. Fienberg H
    11. Wang J
    12. Kambham N
    13. Kirkwood D
    14. Nolan G
    15. Montine TJ
    16. Galli SJ
    17. West R
    18. Bendall SC
    19. Angelo M

    (2019) MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure

    Science Advances 5:eaax5851.

    https://doi.org/10.1126/sciadv.aax5851

    • PubMed
    • Google Scholar
38. 1. Kislauskis EH
    2. Zhu X
    3. Singer RH

    (1994) Sequences responsible for intracellular localization of beta-actin messenger RNA also affect cell phenotype

    The Journal of Cell Biology 127:441–451.

    https://doi.org/10.1083/jcb.127.2.441

    • PubMed
    • Google Scholar
39. 1. Kislauskis EH
    2. Zhu X
    3. Singer RH

    (1997) beta-Actin messenger RNA localization and protein synthesis augment cell motility

    The Journal of Cell Biology 136:1263–1270.

    https://doi.org/10.1083/jcb.136.6.1263

    • PubMed
    • Google Scholar
40. 1. Lawrence JB
    2. Singer RH

    (1986) Intracellular localization of messenger RNAs for cytoskeletal proteins

    Cell 45:407–415.

    https://doi.org/10.1016/0092-8674(86)90326-0

    • PubMed
    • Google Scholar
41. 1. Lécuyer E
    2. Yoshida H
    3. Parthasarathy N
    4. Alm C
    5. Babak T
    6. Cerovina T
    7. Hughes TR
    8. Tomancak P
    9. Krause HM

    (2007) Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function

    Cell 131:174–187.

    https://doi.org/10.1016/j.cell.2007.08.003

    • PubMed
    • Google Scholar
42. 1. Lee BT
    2. Barber GP
    3. Benet-Pagès A
    4. Casper J
    5. Clawson H
    6. Diekhans M
    7. Fischer C
    8. Gonzalez JN
    9. Hinrichs AS
    10. Lee CM
    11. Muthuraman P
    12. Nassar LR
    13. Nguy B
    14. Pereira T
    15. Perez G
    16. Raney BJ
    17. Rosenbloom KR
    18. Schmelter D
    19. Speir ML
    20. Wick BD
    21. Zweig AS
    22. Haussler D
    23. Kuhn RM
    24. Haeussler M
    25. Kent WJ

    (2022) The UCSC genome browser database: 2022 update

    Nucleic Acids Research 50:D1115–D1122.

    https://doi.org/10.1093/nar/gkab959

    • PubMed
    • Google Scholar
43. 1. Lipshitz HD
    2. Smibert CA

    (2000) Mechanisms of RNA localization and translational regulation

    Current Opinion in Genetics & Development 10:476–488.

    https://doi.org/10.1016/s0959-437x(00)00116-7

    • PubMed
    • Google Scholar
44. Preprint

    1. Liu J
    2. Tran V
    3. Vemuri VNP
    4. Byrne A
    5. Borja M
    6. Kim YJ
    7. Agarwal S
    8. Wang R
    9. Awayan K
    10. Murti A
    11. Taychameekiatchai A
    12. Wang B
    13. Emanuel G
    14. He J
    15. Haliburton J
    16. Pisco AO
    17. Neff N

    (2022) Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

    bioRxiv.

    https://doi.org/10.1101/2022.03.04.483068v1

    • Google Scholar
45. 1. Ma W
    2. Mayr C

    (2018) A membraneless organelle associated with the endoplasmic reticulum enables 3′UTR-mediated protein-protein interactions

    Cell 175:1492–1506.

    https://doi.org/10.1016/j.cell.2018.10.007

    • Google Scholar
46. Preprint

    1. Mah CK
    2. Ahmed N
    3. Lopez N
    4. Lam D
    5. Monell A
    6. Kern C
    7. Han Y
    8. Prasad G
    9. Cesnik AJ
    10. Lundberg E
    11. Zhu Q
    12. Carter H
    13. Yeo GW

    (2022) Bento: A Toolkit for Subcellular Analysis of Spatial Transcriptomics Data

    bioRxiv.

    https://doi.org/10.1101/2022.06.10.495510

    • Google Scholar
47. 1. Mao S
    2. Zhang D
    3. Chen L
    4. Tan J
    5. Chu Y
    6. Huang S
    7. Zhou W
    8. Qin H
    9. Xia Q
    10. Zhao Y
    11. Li R
    12. Qin S
    13. Wei M

    (2021) FKBP51 promotes invasion and migration by increasing the autophagic degradation of TIMP3 in clear cell renal cell carcinoma

    Cell Death & Disease 12:899.

    https://doi.org/10.1038/s41419-021-04192-8

    • Google Scholar
48. 1. Marx V

    (2021) Method of the Year: spatially resolved transcriptomics

    Nature Methods 18:9–14.

    https://doi.org/10.1038/s41592-020-01033-y

    • Google Scholar
49. 1. Mayford M
    2. Baranes D
    3. Podsypanina K
    4. Kandel ER

    (1996) The 3’-untranslated region of CaMKII alpha is a cis-acting signal for the localization and translation of mRNA in dendrites

    PNAS 93:13250–13255.

    https://doi.org/10.1073/pnas.93.23.13250

    • PubMed
    • Google Scholar
50. 1. McGeary SE
    2. Lin KS
    3. Shi CY
    4. Pham TM
    5. Bisaria N
    6. Kelley GM
    7. Bartel DP

    (2019) The biochemical basis of microRNA targeting efficacy

    Science 366:eaav1741.

    https://doi.org/10.1126/science.aav1741

    • PubMed
    • Google Scholar
51. Preprint

    1. Meyer E
    2. Dehghannasiri R
    3. Chaung K
    4. Salzman J

    (2021) ReadZS detects developmentally regulated RNA processing programs in single cell RNA-seq and defines subpopulations independent of gene expression

    bioRxiv.

    https://doi.org/10.1101/2021.09.29.462469

    • Google Scholar
52. 1. Meyer E
    2. Chaung K
    3. Dehghannasiri R
    4. Salzman J

    (2022) ReadZS detects cell type-specific and developmentally regulated RNA processing programs in single-cell RNA-seq

    Genome Biology 23:226.

    https://doi.org/10.1186/s13059-022-02795-8

    • PubMed
    • Google Scholar
53. 1. Minis A
    2. Dahary D
    3. Manor O
    4. Leshkowitz D
    5. Pilpel Y
    6. Yaron A

    (2014) Subcellular transcriptomics-dissection of the mRNA composition in the axonal compartment of sensory neurons

    Developmental Neurobiology 74:365–381.

    https://doi.org/10.1002/dneu.22140

    • PubMed
    • Google Scholar
54. 1. Moffitt JR
    2. Hao J
    3. Wang G
    4. Chen KH
    5. Babcock HP
    6. Zhuang X

    (2016) High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization

    PNAS 113:11046–11051.

    https://doi.org/10.1073/pnas.1612826113

    • Google Scholar
55. 1. Moffitt JR
    2. Bambah-Mukku D
    3. Eichhorn SW
    4. Vaughn E
    5. Shekhar K
    6. Perez JD
    7. Rubinstein ND
    8. Hao J
    9. Regev A
    10. Dulac C
    11. Zhuang X

    (2018) Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region

    Science 362:362.

    https://doi.org/10.1126/science.aau5324

    • Google Scholar
56. 1. Moran PAP

    (1950)

    Notes on continuous stochastic phenomena

    Biometrika 37:17–23.

    • PubMed
    • Google Scholar
57. 1. Müller-McNicoll M
    2. Neugebauer KM

    (2013) How cells get the message: dynamic assembly and function of mRNA-protein complexes

    Nature Reviews. Genetics 14:275–287.

    https://doi.org/10.1038/nrg3434

    • PubMed
    • Google Scholar
58. 1. Nitta T
    2. Hata M
    3. Gotoh S
    4. Seo Y
    5. Sasaki H
    6. Hashimoto N
    7. Furuse M
    8. Tsukita S

    (2003) Size-selective loosening of the blood-brain barrier in claudin-5-deficient mice

    The Journal of Cell Biology 161:653–660.

    https://doi.org/10.1083/jcb.200302070

    • PubMed
    • Google Scholar
59. 1. Olivieri JE
    2. Dehghannasiri R
    3. Wang PL
    4. Jang S
    5. de Morree A
    6. Tan SY
    7. Ming J
    8. Ruohao Wu A
    9. Quake SR
    10. Krasnow MA
    11. Salzman J
    12. Tabula Sapiens Consortium

    (2021) RNA splicing programs define tissue compartments and cell types at single-cell resolution

    eLife 10:e70692.

    https://doi.org/10.7554/eLife.70692

    • PubMed
    • Google Scholar
60. 1. Olivieri JE
    2. Dehghannasiri R
    3. Salzman J

    (2022) The SpliZ generalizes “percent spliced in” to reveal regulated splicing at single-cell resolution

    Nature Methods 19:307–310.

    https://doi.org/10.1038/s41592-022-01400-x

    • PubMed
    • Google Scholar
61. 1. Padrón A
    2. Ingolia N

    (2022) Analyzing the composition and organization of ribonucleoprotein complexes by APEX-Seq

    Methods in Molecular Biology 2428:277–289.

    https://doi.org/10.1007/978-1-0716-1975-9_17

    • PubMed
    • Google Scholar
62. 1. Rongo C
    2. Gavis ER
    3. Lehmann R

    (1995) Localization of oskar RNA regulates oskar translation and requires Oskar protein

    Development 121:2737–2746.

    https://doi.org/10.1242/dev.121.9.2737

    • PubMed
    • Google Scholar
63. 1. Saka HA
    2. Valdivia R

    (2012) Emerging roles for lipid droplets in immunity and host-pathogen interactions

    Annual Review of Cell and Developmental Biology 28:411–437.

    https://doi.org/10.1146/annurev-cellbio-092910-153958

    • PubMed
    • Google Scholar
64. 1. Samacoits A
    2. Chouaib R
    3. Safieddine A
    4. Traboulsi A-M
    5. Ouyang W
    6. Zimmer C
    7. Peter M
    8. Bertrand E
    9. Walter T
    10. Mueller F

    (2018) A computational framework to study sub-cellular RNA localization

    Nature Communications 9:4584.

    https://doi.org/10.1038/s41467-018-06868-w

    • PubMed
    • Google Scholar
65. 1. Savojardo C
    2. Martelli PL
    3. Fariselli P
    4. Casadio R

    (2018) DeepSig: deep learning improves signal peptide detection in proteins

    Bioinformatics 34:1690–1696.

    https://doi.org/10.1093/bioinformatics/btx818

    • PubMed
    • Google Scholar
66. 1. Schrimpf C
    2. Xin C
    3. Campanholle G
    4. Gill SE
    5. Stallcup W
    6. Lin S-L
    7. Davis GE
    8. Gharib SA
    9. Humphreys BD
    10. Duffield JS

    (2012) Pericyte TIMP3 and ADAMTS1 modulate vascular stability after kidney injury

    Journal of the American Society of Nephrology 23:868–883.

    https://doi.org/10.1681/ASN.2011080851

    • PubMed
    • Google Scholar
67. 1. Shibuya M
    2. Yamaguchi S
    3. Yamane A
    4. Ikeda T
    5. Tojo A
    6. Matsushime H
    7. Sato M

    (1990)

    Nucleotide sequence and expression of a novel human receptor-type tyrosine kinase gene (flt) closely related to the fms family

    Oncogene 5:519–524.

    • PubMed
    • Google Scholar
68. 1. Ståhl PL
    2. Salmén F
    3. Vickovic S
    4. Lundmark A
    5. Navarro JF
    6. Magnusson J
    7. Giacomello S
    8. Asp M
    9. Westholm JO
    10. Huss M
    11. Mollbrink A
    12. Linnarsson S
    13. Codeluppi S
    14. Borg Å
    15. Pontén F
    16. Costea PI
    17. Sahlén P
    18. Mulder J
    19. Bergmann O
    20. Lundeberg J
    21. Frisén J

    (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

    Science 353:78–82.

    https://doi.org/10.1126/science.aaf2403

    • PubMed
    • Google Scholar
69. 1. Sticht C
    2. De La Torre C
    3. Parveen A
    4. Gretz N

    (2018) miRWalk: An online resource for prediction of microRNA binding sites

    PLOS ONE 13:e0206239.

    https://doi.org/10.1371/journal.pone.0206239

    • PubMed
    • Google Scholar
70. 1. Stickels RR
    2. Murray E
    3. Kumar P
    4. Li J
    5. Marshall JL
    6. Di Bella DJ
    7. Arlotta P
    8. Macosko EZ
    9. Chen F

    (2021) Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2

    Nature Biotechnology 39:313–319.

    https://doi.org/10.1038/s41587-020-0739-1

    • PubMed
    • Google Scholar
71. 1. Su G
    2. Qin X
    3. Enninful A
    4. Bai Z
    5. Deng Y
    6. Liu Y
    7. Fan R

    (2021) Spatial multi-omics sequencing for fixed tissue via DBiT-seq

    STAR Protocols 2:100532.

    https://doi.org/10.1016/j.xpro.2021.100532

    • PubMed
    • Google Scholar
72. 1. Suter B

    (2018) RNA localization and transport

    Biochimica et Biophysica Acta. Gene Regulatory Mechanisms 1861:938–951.

    https://doi.org/10.1016/j.bbagrm.2018.08.004

    • PubMed
    • Google Scholar
73. 1. Tang Q
    2. Nie F
    3. Kang J
    4. Chen W

    (2021) mRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy

    Molecular Therapy 29:2617–2623.

    https://doi.org/10.1016/j.ymthe.2021.04.004

    • PubMed
    • Google Scholar
74. Conference

    1. Thomas RM
    2. John J

    (2017) A review on cell detection and segmentation in microscopic images

    2017 International Conference on Circuit,Power and Computing Technologies (ICCPCT).

    https://doi.org/10.1109/ICCPCT.2017.8074189

    • Google Scholar
75. 1. Vicar T
    2. Balvan J
    3. Jaros J
    4. Jug F
    5. Kolar R
    6. Masarik M
    7. Gumulec J

    (2019) Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison

    BMC Bioinformatics 20:360.

    https://doi.org/10.1186/s12859-019-2880-8

    • PubMed
    • Google Scholar
76. Website

    1. Vizgen

    (2024) Data release program-vizgen

    Accessed April 20, 2027.

    https://vizgen.com/data-release-program/
77. 1. Weber BHF
    2. Vogt G
    3. Pruett RC
    4. Stöhr H
    5. Felbor U

    (1994) Mutations in the tissue inhibitor of metalloproteinases-3 (TIMP3) in patients with Sorsby’s fundus dystrophy

    Nature Genetics 8:352–356.

    https://doi.org/10.1038/ng1294-352

    • Google Scholar
78. 1. Westrich JA
    2. Vermeer DW
    3. Colbert PL
    4. Spanos WC
    5. Pyeon D

    (2020) The multifarious roles of the chemokine CXCL14 in cancer progression and immune responses

    Molecular Carcinogenesis 59:794–806.

    https://doi.org/10.1002/mc.23188

    • PubMed
    • Google Scholar
79. 1. Wilson SC
    2. White KI
    3. Zhou Q
    4. Pfuetzner RA
    5. Choi UB
    6. Südhof TC
    7. Brunger AT

    (2019) Structures of neurexophilin-neurexin complexes reveal a regulatory mechanism of alternative splicing

    The EMBO Journal 38:e101603.

    https://doi.org/10.15252/embj.2019101603

    • PubMed
    • Google Scholar
80. 1. Wolf FA
    2. Angerer P
    3. Theis FJ

    (2018) SCANPY: large-scale single-cell gene expression data analysis

    Genome Biology 19:15.

    https://doi.org/10.1186/s13059-017-1382-0

    • PubMed
    • Google Scholar
81. 1. Xia C
    2. Fan J
    3. Emanuel G
    4. Hao J
    5. Zhuang X

    (2019) Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression

    PNAS 116:19490–19499.

    https://doi.org/10.1073/pnas.1912459116

    • Google Scholar
82. 1. Xue ZZ
    2. Wu Y
    3. Gao QZ
    4. Zhao L
    5. Xu YY

    (2020) Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

    BMC Bioinformatics 21:398.

    https://doi.org/10.1186/s12859-020-03731-y

    • PubMed
    • Google Scholar
83. 1. Yao Z
    2. Liu H
    3. Xie F
    4. Fischer S
    5. Adkins RS
    6. Aldridge AI
    7. Ament SA
    8. Bartlett A
    9. Behrens MM
    10. Van den Berge K
    11. Bertagnolli D
    12. de Bézieux HR
    13. Biancalani T
    14. Booeshaghi AS
    15. Bravo HC
    16. Casper T
    17. Colantuoni C
    18. Crabtree J
    19. Creasy H
    20. Crichton K
    21. Crow M
    22. Dee N
    23. Dougherty EL
    24. Doyle WI
    25. Dudoit S
    26. Fang R
    27. Felix V
    28. Fong O
    29. Giglio M
    30. Goldy J
    31. Hawrylycz M
    32. Herb BR
    33. Hertzano R
    34. Hou X
    35. Hu Q
    36. Kancherla J
    37. Kroll M
    38. Lathia K
    39. Li YE
    40. Lucero JD
    41. Luo C
    42. Mahurkar A
    43. McMillen D
    44. Nadaf NM
    45. Nery JR
    46. Nguyen TN
    47. Niu S-Y
    48. Ntranos V
    49. Orvis J
    50. Osteen JK
    51. Pham T
    52. Pinto-Duarte A
    53. Poirion O
    54. Preissl S
    55. Purdom E
    56. Rimorin C
    57. Risso D
    58. Rivkin AC
    59. Smith K
    60. Street K
    61. Sulc J
    62. Svensson V
    63. Tieu M
    64. Torkelson A
    65. Tung H
    66. Vaishnav ED
    67. Vanderburg CR
    68. van Velthoven C
    69. Wang X
    70. White OR
    71. Huang ZJ
    72. Kharchenko PV
    73. Pachter L
    74. Ngai J
    75. Regev A
    76. Tasic B
    77. Welch JD
    78. Gillis J
    79. Macosko EZ
    80. Ren B
    81. Ecker JR
    82. Zeng H
    83. Mukamel EA

    (2021) A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

    Nature 598:103–110.

    https://doi.org/10.1038/s41586-021-03500-8

    • PubMed
    • Google Scholar
84. 1. Yisraeli JK

    (2005) VICKZ proteins: a multi‐talented family of regulatory RNA‐binding proteins

    Biology of the Cell 97:87–96.

    https://doi.org/10.1042/BC20040151

    • Google Scholar
85. 1. Zappulo A
    2. van den Bruck D
    3. Ciolli Mattioli C
    4. Franke V
    5. Imami K
    6. McShane E
    7. Moreno-Estelles M
    8. Calviello L
    9. Filipchyk A
    10. Peguero-Sanchez E
    11. Müller T
    12. Woehler A
    13. Birchmeier C
    14. Merino E
    15. Rajewsky N
    16. Ohler U
    17. Mazzoni EO
    18. Selbach M
    19. Akalin A
    20. Chekulaeva M

    (2017) RNA localization is a key determinant of neurite-enriched proteome

    Nature Communications 8:583.

    https://doi.org/10.1038/s41467-017-00690-6

    • PubMed
    • Google Scholar
86. Preprint

    1. Zhang M
    2. Eichhorn SW
    3. Zingg B
    4. Yao Z
    5. Zeng H
    6. Dong H
    7. Zhuang X

    (2020) Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomics

    bioRxiv.

    https://doi.org/10.1101/2020.06.04.105700

    • Google Scholar
87. 1. Zhang M
    2. Eichhorn SW
    3. Zingg B
    4. Yao Z
    5. Cotter K
    6. Zeng H
    7. Dong H
    8. Zhuang X

    (2021) Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH

    Nature 598:137–143.

    https://doi.org/10.1038/s41586-021-03705-x

    • PubMed
    • Google Scholar

Author details

1. Rob Bierman

   Department of Biochemistry Stanford University, Stanford, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8513-7425

2. Jui M Dave

   Department of Biomedical Data Science Stanford University, New Haven, United States

   Contribution

   Formal analysis, Validation, Investigation, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

3. Daniel M Greif

   Department of Biomedical Data Science Stanford University, New Haven, United States

   Contribution

   Resources, Supervision, Validation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9842-3751

4. Julia Salzman

   1. Department of Biochemistry Stanford University, Stanford, United States
   2. Departments of Medicine (Cardiology) and Genetics Yale University, New Haven, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing

   For correspondence

   julia.salzman@stanford.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7630-3436

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