Xist and multiple autosomal genes in mouse kidney. By combining these data with mathematical modeling, we evaluated models for allele-specific heterogeneity, in particular demonstrating that apparent expression from only one of the alleles in single cells can arise as a consequence of low-level mRNA abundance and transcriptional bursting.Extensive cell-to-cell variation exists even among putatively identical cells, and there is great interest in understanding how the properties of transcription relate to this heterogeneity. Differential expression from the two gene copies in diploid cells could potentially contribute, yet our ability to measure from which gene copy individual RNAs originated remains limited, particularly in the context of tissues. Here, we demonstrate quantitative, single molecule allele-specific RNA FISH adapted for use on tissue sections, allowing us to determine the chromosome of origin of individual RNA molecules in formaldehyde-fixed tissues. We used this method to visualize the allele-specific expression of  In mammals, most cells of the body contain two genetic datasets: one from the mother and one from the father, and—in theory—these two sets of information could contribute equally to produce the molecules in a given cell. In practice, however, this is not always the case, which can have dramatic implications for many traits, including visible features (such as fur color) and even disease outcomes. However, it remains difficult to measure the parental origin of individual molecules in a given cell and thus to assess what processes lead to an imbalance of the maternal and paternal contribution. We adapted a microscopy technique—called allele-specific single molecule RNA FISH—that uses a combination of fluorescent tags to specifically label one type of molecule, RNA, depending on its origin, for use in mouse kidney sections. Focusing on RNAs that were previously reported to show imbalance, we performed measurements and combined these with mathematical modeling to quantify imbalance in tissues and explain how these can arise. We found that we could recapitulate the observed imbalances using models that only take into account the random processes that produce RNA, without the need to invoke special regulatory mechanisms to create unequal contributions. Gene expression in genetically identical individual cells often deviates from that of the cell population average , which ieg. [An additional nuance to the effects of bursting on cellular variability is that diploid mammalian cells carry two sets of chromosomes (one from each parent), which means that they also have two copies of each individual gene. It is typically assumed that for most genes both copies, called alleles, are capable of being expressed, thus providing protection through redundancy if one of them is mutated ,18. Receeg. –34) and eg. ,36.Beyond these prototypic cases, it has been proposed that many more autosomal genes may be subject to random monoallelic expression –43, but in vivo requires a dedicated mechanism or if it could arise as a consequence of transcriptional bursting.To resolve this question of mechanism, Reinius et al proposed an elegant scenario , whereby the many genes with random monoallelic expression may arise not by differential cell- and allele-specific regulation, but instead monoallelic expression may arise by chance ,44. In tin situ hybridization technique that is sensitive to single-nucleotide differences between RNAs for the analysis of transcripts in snap-frozen, cryosectioned tissues from different mouse strains and their hybrids [in vivo expression levels. We used this method to measure allele-specific expression of multiple autosomal genes and of Xist, a gene for which it is well-documented that individual cells randomly and exclusively transcribe either the maternal or the paternal copy. Quantitative analysis of the data enabled us to answer whether the autosomal genes we investigated were expressed from one or both gene copies in single cells in tissue. While we observed monoallelic expression in some cells, mathematical modeling showed that this pattern was compatible with random transcriptional events  from the two alleles producing low levels of RNA, rather than an explicit mechanism governing random monoallelic expression.In this study, we adapted a previously described single-molecule RNA fluorescent  hybrids –52. Thisin situ in cultured cells  inactivated X chromosome, is expressed at high levels, and contains a large number of SNVs between the two substrains, making it an ideal test case for our method. We collected kidney tissues from mice at day 4 of postnatal development, snap-froze them in liquid nitrogen or on aluminium blocks in dry ice and cryosectioned them at 5 μm thickness. We then applied our in situ hybridization protocol using allele-specific probes. As expected, we observed the appropriate sex-specific expression pattern: no fluorescent signal in male tissues . These genes were selected to represent genes with  and without  putative random monoallelic expression [Aqp11 , Mpp5 55,56, Prc) . When we assayed the overall colocalization rates for these autosomal genes in kidney sections, we found that 4 out of 8 genes had mean colocalization rates >50% , and we therefore do not know the binding behaviour of individual probes.Having verified that we could correctly measure the allelic origin of clusters of Xist RNA accumulated on the X chromosome in mouse tissues, we next ascertained whether the method would also work for monodisperse spots corresponding to single RNA molecules, which is how most mRNAs appear in the cell. We considered this challenging, because single, punctate RNA spots would be both smaller and considerably dimmer compared to Xist RNA, which accumulates multiple copies on the inactivated X chromosome. Thus, to test our protocol for use on single RNA molecules, we designed probes for 8 autosomal genes that contained at least one polymorphism in BL6 versus either JF1 or the C7 strain  and single molecule RNA spots in the context of whole tissue sections. This motivated us to ask if we could directly quantify cell-to-cell heterogeneity in the allelic origin of RNAs in tissue.Xist, we investigated the spatial clustering of cells based on their X inactivation choice, i.e. to what extent cells expressing Xist from either the JF1 vs BL6 X chromosome intermix. For the autosomal genes, we quantified their allelic imbalance in single cells, i.e. whether individual cells expressed RNAs from the two chromosomes at different ratios.To determine how the chromosomal origin of RNAs contributes to cell-to-cell heterogeneity in tissue, we focused on two different questions. For Xist from the same chromosome clustered together in small patches in tissue sections, resulting in a spatially fairly mixed population of cells and showed no evidence of extended patches with the same allele-specific expression.In the case of Xist, we measured spatial clustering of allele-specific expression because each cell is randomly and fully committed to expressing RNA from only the BL6 or the JF1 chromosome. Thus, allelic imbalance in tissue is not due to quantitative expression differences between the two alleles in single cells. Instead, it can arise either as a consequence of overall skewing of X chromosome inactivation rates or due to uneven spatial distribution of cells with a given inactivated X chromosome. Such spatial partitioning can arise through the local expansion of cells in which X chromosome inactivation “choice” has already been fixed, resulting in extended patches of cells carrying the same inactive X chromosome –61. BecaAebp1, Lyplal1, Mpp5) where >50% of guide spots colocalized with signal from SNV probes . These RNA counts measured by RNA FISH were higher than those observed via single-cell RNA sequencing of the kidney [Aebp1, 1.0–1.2 UMI/cell for Lyplal and 1.0–1.63 UMI/cell for Mpp5, depending on the cell type, considering only those cells where transcripts for these genes were detected . We selNA spots . First, e kidney . The meadetected . Given tAebp1 and Lyplal1, because these two genes had been previously identified as genes with putative random monoallelic expression in other tissues [We predominantly detected the correct allele in the homozygous kidney samples, both in bulk and at the single cell level . In hete tissues –41. StilAebp1, 0.29 for Lyplal1 and 0.35 for Mpp5 suggesting somewhat coordinated expression. To better understand how these values compare to the expectations from different models of transcription, we initially considered two extreme cases: an “all-or-none” scenario in which every cell has transcripts exclusively from one or the other gene copy, and a “coin flip” scenario in which the allelic origin of every individual transcript is essentially indistinguishable from random coin flipping (Xist), whereas the latter corresponds to a null model with no distinct allele-specific transcriptional regulation. We used computational modeling to simulate these two scenarios and to discriminate between them.Our results showing that individual cells could have mRNA from either one or both alleles motivated us to assess whether existing models of transcription were sufficient to explain the observed cell-to-cell variability in allelic imbalance. To evaluate these models, we first used the correlation coefficient between the BL6 and JF1 mRNA expression in our population as a simple metric that captures the joint behaviour of the two alleles: a correlation coefficient of zero represents no coordinated expression between the two alleles, positive values indicate coordinated expression, while negative correlation could be indicative of anti-correlation or repulsion between the two alleles. The correlation between BL6 and JF1 mRNA counts were 0.41 for flipping . The forversus simulated cell populations was also different  many cells have mRNAs from both gene copies, which was seemingly incompatible with this scenario. However, it was still formally possible that cells in reality only had transcripts exclusively from one of the gene copies and that the apparent transcripts from the other copy were technical artifacts due to false detection events. We used homozygous tissue to measure the rate at which these false detection events occur, and thereby estimated the expected false detection rate in heterozygous cells. We then computationally simulated hypothetical “all-or-nothing” heterozygous cell populations taking into account these false detection rates, and found that the RNA counts and distributions observed in such a simulated all-or-none population were still inconsistent with our data compare . Next, wifferent . Collectversus simulated single-cell RNA distributions looked very similar for all three genes  cells with Aebp1 transcription sites) showed simultaneous expression from both alleles. This corroborated that cells indeed produced Aebp1 in transcriptional bursts and that it was possible for individual cells to transcribe RNA from both alleles simultaneously.To test whether transcriptional bursting could explain the observed data, we first wanted to confirm that c probes , which, i.e., whether for per-cell RNA counts from the two alleles was more or less correlated than one would expect by chance. To simulate the null hypothesis of no interaction between alleles we randomly paired up the modeled BL6 and JF1 counts, mimicking cells that contain RNA from both alleles. When we compared this simulation to the real data we consistently observed that the modeled per cell BL6 and JF1 counts were less correlated than the real pairwise measurements  and burst frequency for the two alleles based on the observed RNA counts for each allele independently and used these parameters as inputs for our model see . We thenurements . Thus, wurements . TogetheAebp1, Lyplal1 and Mpp5 the observed monoallelic expression in some single cells can likely be explained by low levels of transcription occurring randomly from the two gene copies without having to invoke a special mechanism that limits expression to one of the alleles.Thus, for in situ hybridization method described here is a step in this direction: by visualizing endogenous SNVs it enables the assignment of single RNA molecules to their allele of origin in single cells and in the context of whole tissue sections. We now provide quantitative information about cell-to-cell heterogeneity with single transcript resolution, which is an extension of our previous work, where we used this method for a more qualitative assessments (i.e. presence-absence) of parental origin of mRNA in tissue [e.g. in the case of Aebp1). Thus, we did not have to invoke a special mechanism that restricted expression to only one allele to explain the presence of cells with either BL6 or JF1 monoallelic expression status.There has been great interest in recent years to precisely measure expression from the two alleles of a gene in diploid cells, ideally directly in tissue and at the single-cell level. The RNA fluorescence n tissue . When weMpp5, this conclusion is in line with the fact that the reported haploinsufficient phenotype is thought to be caused by overall reduced dosage [Aebp1 and Lyplal1, which had previously been identified to display random monoallelic expression, our data suggest that no additional mechanism is needed to explain the presence of cells with monoallelic expression in kidney tissue. However, we cannot extrapolate these results to other tissues and mouse strains, given that random monoallelic expression is tissue specific. Thus, we cannot conclude whether mechanisms for allele-specific regulation or for maintenance of monoallelic status are present in the mouse strains and cell types used in the studies that originally identified genes with random monoallelic expression [Our results suggest that cells with RNA expression from only one of the alleles occur due to the low levels of expression and thus the limited number of random sampling events from the two gene copies. Because transcription is a dynamic process this state is likely transient so that while a cell may have mRNA from only one allele at a particular time, it may gain mRNA from the other allele a short time later if another transcriptional event occurs. For d dosage ,56 rathed dosage ,44, and pression –41. Intepression –40, whicAebp1, Mpp5 and Lyplal1, we also demonstrated our ability to distinguish Xist expression from the BL6 vs JF1 chromosome and to assess the spatial relationship of cells expressing different parental alleles. We detected a spatially fairly mixed population in the kidney, where cells expressing Xist from the same chromosome clustered together in small patches in transverse sections. This is contrary to other tissues, such as intestinal crypts or the skin [In addition to the data on the skin ,59,61,64the skin ,66. RegaXist expression from the BL6 and JF1 allele, the probes were designed so that they should distinguish equally well between the 129-strains and CAST/EiJ, which are also commonly used to study strain-specific expression.Through the examples detailed above we have shown how our method can be used to directly quantify cell-to-cell differences that arise due to differential expression from the two alleles in diploid cells. Our approach overcomes multiple limitations imposed by previous methods: First, because this method enables sensitive SNV-specific detection of even single mRNA molecules it provides more information than RNA FISH measurements that rely solely on quantifying the number of transcription sites in individual cells ,40,41,67in situ analysis of A-to-I RNA editing [Churc1, a gene that is consistently expressed at low levels in all cell types of the kidney . It is technically possible to determine colocalization events at this mRNA density, though a large number of cells needs to be screened for robust quantification. The upper boundary is based on the observation that a high density of transcripts makes it impossible to precisely assign colocalization. In our experiments only Podxl in podocytes showed such high expression levels. Based on the relatively low proportion of podocytes in the kidney we determined that this matched an expression level of >800FPKM in these cells (based on bulk sequencing data) and an average count of 6.9 UMI/cell in single-cell sequencing data. Importantly, given the higher detection efficiency of RNA per cell, allele-specific single-molecule RNA FISH provides a valuable complementary tool to single-cell transcriptomics for low-to-moderate expression level genes, and will likely be useful in confirming findings made by sequencing.In addition, while our quantitative analysis focused solely on genes with a relatively high number of SNVs and high mean colocalization rates (>50%), it should be noted that we did not systematically explore the relationship between SNVs and colocalization rates, and also that lower colocalization can be sufficient to address specific questions, as was demonstrated in a recent single cell  editing . It is tin vivo by chance alone. Beyond this application, our methods could have a number of additional uses. Similar analyses could be performed in other tissues and, for example, could enable the evaluation of genetic variants directly in the tissue believed to be affected if there are genic SNVs in linkage with those variants or to study mutations thought to lead to haploinsufficiency. Furthermore, with single cell resolution, our method allows for the interrogation of particular cellular subtypes within a tissue. In concert with recent genome-wide association studies in single cells [In conclusion, we demonstrated how quantitative measurement of allele-specific expression in tissue could be used to directly determine the level of allelic imbalance in single cells. By combining these measurements with modeling, we showed that random monoallelic expression could arise le cells ,77, thisC57BL/6J and JF1/Ms founder mice were obtained from Jackson Laboratories. All mouse work was conducted in accordance with the University of Pennsylvania Institutional Animal Care and Use Committee. For tissue collection we used either homozygous C57BL/6J or JF1/Ms pups, or F1 heterozygotes from both C57BL/6J x JF1/Ms or JF1/Ms x C57BL/6J crosses. We dissected pups at postnatal day 4 using standard techniques, and mounted tissues in Tissue-Plus O.C.T. compound , flash-froze them in liquid nitrogen or on an aluminum block in dry ice, and then stored tissues at −80°C. We determined sex of the animals by visual inspection and verified this by SRY-specific PCR on DNA extracted from a tail sample, collected during dissection. Tissues were cryosectioned at 5 μm using a Leica CM1950 cryostat. We adhered tissue samples to positively charged Colorfrost plus slides (Fisher Scientific), washed slides in PBS, fixed them in 4% formaldehyde for 10 min at room temperature, then washed again in PBS two times. Fixed slides were stored in 70% ethanol at 4°C.https://www.ebi.ac.uk/gxa/). We later established that this corresponded to expression levels of >10FPKM in bulk and 0.1–1 UMI/cell (in the tissue(s) with highest expression; Churc1 , for which we were nevertheless able to robustly detect transcripts using the guide probes , and identify colocalization events above random (pixel-shifted) controls. The only gene with higher expression was Podxl with a UMI of 6.9 per cell in podocytes.We shortlisted genes that had previously been identified in studies of random monoallelic expression, genes with known function and/or phenotypes in kidney and genes with documented expression in different cell types in the kidney. We then filtered shortlisted genes for expression levels of >20 TPM in publicly available bulk kidney sequencing data , accessehttp://molossinus.lab.nig.ac.jp/msmdb/index.jsp) [Aebp1 and Lyplal1 we confirmed these SNVs through PCR amplification and sequencing of exonic sequences of genomic JF1/Ms DNA. All guide probes and the Aebp1 intron probe set were designed using the Stellaris probe designer (Biosearch Technologies), SNV-specific probes were designed as specified in Levesque et al. [To identify exonic SNVs between the C57BL/6J and JF1/Ms strains we used the NIG Mouse Genome Database (dex.jsp) . For Aebe et al.  and maskAebp1 and Lyplal1, we designed primers for the exonic segments of these genes (primer sequences available in 2 according to the manufacturer's instructions. PCR amplicons were purified with ExoSAP-IT (ThermoFisher) and submitted for sequencing to the University of Pennsylvania DNA sequencing facility. For sex-specific genotyping of pups we used Sry-specific primers (DNA was extracted from tail biopsies using a quick-lyse protocol: 100μl of Solution A (25mM NaOH and 0.2mM EDTA) were added to the tissue and kept at 95°C for 30 min, before adding an equal volume of Solution B . Samples were then spun at 6000 rpm for 10 min and 100μl of the top layer was transferred to a fresh tube. 1μl of this solution was used as template for PCR. To verify the presence of reported SNVs in  primers , since tMus musculus subspecies  [Allele-specific RNA fluorescence in situ hybridization works by first detecting the mRNA of interest  using conventional single molecule RNA FISH probes labelled in one color (guide probes), and then colocalizing this signal with probes that discriminate specific single-nucleotide differences based on a “toehold probe” strategy and which are labelled in colors unique to the two different alleles. In this way, mRNAs are essentially “tagged” as being either from one or the other parental chromosome. In cultured cells, this approach can successfully distinguish RNA variants that contain just one single nucleotide variant (SNV) and thus can only be targeted with a single variant-specific probe ,51, but ctively) ,82. Due ctively) , allowinGapdh probes (labelled with Atto488). To stain the samples, we first washed the slides with tissues sections in 2x SSC, then incubated them in 8% SDS for 2 minat room temperature, washed again in 2x SSC and finally added the hybridization buffer with probes. Slides were covered with coverslips and left to hybridize overnight in a humidified chamber (ibidi) at 37°C. The next morning we performed two 30 min washes in wash buffer , the second one including DAPI to stain nuclei. To label cell membranes (to clearly identify single cells) the first wash was sometimes substituted with a 15 min incubation in wash buffer containing wheat germ agglutinin coupled with Alexa488 (LifeTech) and a 15 min regular wash. After the final wash, slides were rinsed twice with 2x SSC and once with antifade buffer (10 mM Tris (pH 8.0), 2× SSC, 1% w/v glucose). Finally, slides were mounted for imaging in antifade buffer with catalase and glucose oxidase [For each gene of interest we first prepared a probe mix, containing a guide probe set , the two allele-specific probe sets  and a set of mask oligos  in hybridization buffer . For detection of nascent Aebp1 RNA we also included intronic probes labelled either with Atto488 or Atto700, and to verify the integrity of RNA in male tissues stained for Xist we also included  oxidase  to prevez-stacks (0.3 μm spacing between stacks) of stained cells in six different fluorescence channels using filter sets for DAPI, Atto 488, Cy3, Calfluor 610, Cy5, and Atto 700. The filter sets we used were 31000v2 (Chroma), 41028 (Chroma), SP102v1 (Chroma), 17 SP104v2 (Chroma), and SP105 (Chroma) for DAPI, Atto 488, Cy3, Cy5, and Atto 700, respectively. A custom filter set was used for CalFluor610 (Omega).We imaged all samples on a Nikon Ti-E inverted fluorescence microscope using either a 60x or a 100× Plan-Apo objective and a cooled CCD camera (Andor iKon 934). For whole-tissue scans we imaged at 60x and used Metamorph imaging software (Scan Slide application) to acquire a tiled grid of images. We used the Nikon Perfect Focus System to ensure that the images remained in focus over the imaging area. For 100× imaging, we acquired For Xist image analysis we worked with whole tissue scans, where we had collected data for Cal fluor 610 (Xist guide probes), Cy3 and Cy5  and DAPI (nuclei). To visualize scans, we used the “Grid/Collection stitching” feature available in Fiji  to assemi.e. 16 tiles all 1/16 of the full scan size). For each rectangle, we calculated the fraction of cells expressing Xist from the BL6 allele. Next, we obtained the variance of these BL6 cell fraction values across all rectangles of a given size. This protocol was repeated for different sizes of rectangles ranging from 16 to 256 rectangles spanning the entire tissue section. We also calculated a baseline for spatial heterogeneity of random allelic choice by repeating this analysis on 1000 random permutations of the data for each sample generated by MATLAB’s randperm. We performed a similar analysis to determine the minimal cluster size of Xist foci with identical allelic identity, but instead of random permutations we generated simulations, where kidney sections were randomly seeded with clusters of a fixed size (ranging from 1 to 10) while keeping the allelic ratio the same as for the measured data. For each seed size we generated 500 simulations. To obtain a likely minimal cluster size for cells with identical X chromosome inactivation we selected the seed size whose variance deviated least from the variance observed for the real data. We repeated this process for each subdivision size and determined the mean across all subdivision sizes.To assess spatial patterns of Xist allelic choice we then used the positional and identity information from our automatic assignments, and developed a metric for spatial heterogeneity. First, we tiled images into regular rectangles of equal size . To quantify colocalization rates we first determined the ideal colocalization radius for each gene. To do so, we segmented extended areas of the tissue . To ascertain subpixel-resolution spot locations the software then fitted each spot to a two-dimensional Gaussian profile specifically on the z plane on which the spot occured. Next, colocalization between guide spots and allele-specific spots was determined in two stages. In the first stage, we searched for the nearest-neighbor allele-specific probe for each guide spots within a 2.5-pixel (360-nm) window and ascertain the median displacement vector field, which was subsequently used to correct for chromatic aberrations. After this correction, we tested a range of different radii (r = 0.1 to 2.5 pixel) for each gene to calculate colocalization rates for the real data, as well for pixel-shifted data, where we took our images and shifted the guide channel by adding 2*r pixels to the X and Y coordinates. This pixelshifted data was used to test random colocalization due to spurious allele-specific spots. For each gene we then visually inspected colocalization rates for real and pixel-shifted data at the different radii and determined a radius where both the colocalization rate for the real data and the difference between the real and the pixel-shifted data was maximal. The selected colocalization radii for each gene are included in For analysis of single molecule RNA spots we used a combination of 60x whole tissue scans in DAPI and Cal fluor 610 to determine the overall structure of the tissue and collecting z-stacks at 100x resolution of 5–10 individual positions within that tissue to identify individual mRNA molecules and characterize their allelic identity. To determine allelic identity we first segmented and thresholded images using a custom MATLAB software suite (downloadable at https://bitbucket.org/arjunrajlaboratory/rajlabimagetools/wiki/Home). For this, we segmented cells, thresholded RNA FISH signal and identified transcription sites for Aebp1 by co-localization of spots in the intron and exon channel. Relative fluorescence intensities of transcription sites vs cytoplasmic RNAs were determined based on the fluorescence intensity of the guide probes using custom scripts written with R packages dplyr and ggplot2.Transcription site analysis was performed using a custom MATLAB software suite  differed by 7% or less between the dye combinations for all three genes, with the exception of Mpp5 in BL6 homozygous tissue. Similarly, the false detection rate, which includes both the error from incorrect probe binding and the differential detection sensitivity, deviated by less than 4% between the dye combinations for all three genes, with the exception of Mpp5 in BL6 homozygous tissue. For Mpp5 in BL6 homozygous tissue we observed that the BL6 allele was detectable at higher efficiency when labelled with Cy5 (66%) than when labelled with Cy3 (50%). We therefore also collected dye-swapped data for Mpp5 in heterozygous tissues . We therefore collected extensive data for Aebp1 in primary fibroblasts, where Aebp1 is expressed at higher levels (10s to 100s of RNAs per cell) to determine the general effect of the colocalization rate on allelic ratios. In homozygous fibroblasts we found that there was an inverse relationship between colocalization rate and false detection rate, so that higher colocalization rate corresponded with a lower false detection rate and a higher rate of calling the correct allele . We then randomly downsampled the RNA in each cell to the number of RNAs that had been assigned a unique BL6 or JF1 identity in the original measurement. Each simulation was performed 5000 times. We found that the log likelihoods and correlations from these downsampled simulations were essentially indistinguishable from the original simulations performed without downsampling , we let nBL6j be the number of transcripts with C57BL/6J identity detected in cell j and nJF1j be the number the number transcripts with JF1/Ms identity detected in cell j. Under the null model, nBL6j was drawn from a binomial with (nBL6j + nJF1j) draws and probability pBL6. We simulated single-cell label counts for cells by drawing from these conditional null distributions for each cell 10,000 times. We then compared the negative log-likelihood of the observed data with the distribution of negative log-likelihoods of each simulation iteration.To determine how the observed data compared to random monoallelic expression  or binomially distributed (coin-flip scenario) allelic calls we simulated those scenarios through modelling. For the binomial distribution we considered a null model wherein all heterozygous cells share the same allelic ratio, which was determined to be the overall allelic ratio observed at the population level. Then, for an experiment with overall estimated C57BL/6J allelic ratio equal to To simulate random monoallelic expression each cell was assigned either a BL6 or a JF1 identity, based either on the majority of RNAs in a cell, or based on random assignment if both alleles had the same count. We then designated all RNAs in a given cell to the same allelic identity . Next, we randomly added “technical noise” 10,000 times, by changing some RNAs in the population to the opposite identity, based on the false positive rates measured in the original BL6 and JF1 homozygous populations. These steps were performed while keeping the final overall RNA assignments in the population the same as the original heterozygous population. For the 10,000 simulations we then calculated negative log likelihoods similarly as we did for the binomial distributions, assuming two separate null models for the BL6 and JF1 populations, whose parameters were determined by the original homozygous population.p = mean/variance and r = mean^2/(variance-mean)), from which we obtained the burst size and frequency using: burst_size = (1-p)/p and burst_frequency = r. We then generated 10,000 RNA counts for the two alleles separately by drawing from a distribution with the r and p parameters we had calculated. We visualized the obtained mRNA counts for both alleles individually using a randomly selected simulation, and also calculated the negative log-likelihood distribution of the 10,000 simulated datasets. Next, we randomly paired the data for the two alleles to generate “cells” with RNA counts from both alleles and calculated the correlation between the BL6 and JF1 counts in each of these modeled cells. In addition to using the negative binomial parameters that we had calculated from our data, we also tested a series of additional burst sizes (from 0.5 to 5 RNAs per burst) and repeated the entire analysis, which showed that our findings were consistent across a range of burst values .Raw and processed data, as well as scripts for all analyses presented in this paper, including all data extraction, processing, and graphing steps are freely accessible from the Open Science Framework  is available here:https://bitbucket.org/arjunrajlaboratory/rajlabimagetools/wiki/HomeAll animal studies were approved by the Institutional Animal Care and Use Committee at the University of Pennsylvania (IACUC protocol 805433). The animals used in this study were treated humanely and with regard for alleviation of suffering.S1 FigA. Male heterozygous (BL6 x JF1) tissue was stained with probes for Xist , BL6-specific probes labelled with Cy5 (middle left), JF1-specific probes with Cy3 (middle right)), none of which showed signal above background. Atto 488-labelled Gapdh probes were also included to verify absence of RNA degradation (far right). B, C. Female BL6 homozygous (B) and JF1 homozygous (C) tissues were stained with probes for Xist , BL6-specific probes labelled with Cy3 (middle left), JF1-specific probes with Cy5 (middle right)). Right-most image shows colocalization between guide foci and allele-specific foci as indicated.(TIF)Click here for additional data file.S2 FigA. Allelic assignments across different BL6 and JF1 homozygous (far left and far right) as well as heterozygous (middle) kidney sections. Two of the heterozygous kidney sections are technical replicates , which is indicated by an asterisk (“*”). BL6 allelic assignment is depicted in turquoise, JF1 allelic assignment is depicted in orange. B. For all heterozygous samples we calculated spatial heterogeneity using a variance metric, the method of which is schematized: sections were subdivided into a grid, using increasingly smaller squares (from 8x8 to 16x16) and for each subdivision we calculated the ratio of BL6 Xist foci. For each grid we then also calculated the variance of the BL6 ratio across all squares of that grid. C. The measured variance (red line) was compared to the variances obtained for samples where we randomly permuted allelic assignments 1000 times . The graphs show the variance for subdivisions of different sizes, with both the area of the subdivisions and the size of the grid indicated. D. Measured variance (red line) was also compared to the variances of samples where we randomly placed different sized clusters (seeds) of allelically identical Xist foci in the tissue . For each seed size we generated 500 randomizations, keeping the allelic ratio constant. For all heterozygous data shown in A, C and D the order of the samples is kept identical.(TIF)Click here for additional data file.S3 FigA. FPKM values of six control samples from Beckerman et al [B. UMI counts per cell for Aebp1, Lyplal1 and Mpp5 for cells with non-zero UMIs, based on data from Park et al [an et al  are showrk et al .(TIF)Click here for additional data file.S4 FigA, B. Overall (A) and allele-specific (B) colocalization rates for different autosomal genes. Overall colocalization rates consider all guide spots that colocalize with either BL6 and/or JF1/C7 allele-specific signal, while allele-specific colocalization counts only those guide spots that colocalize uniquely with either BL6 or JF1/C7 probes. Each spot represent the colocalization rate in one area tested . All genes were detected with guide probes labelled with Cal fluor 610, and the following allele-specific probes: Aebp1, Mpp5 and Podxl BL6-specific probes labelled with Cy3, JF1-specific probes labelled with Cy5; Churc1 and Lyplal1 BL6-specific probes labelled with Cy5, JF1-specific probes labelled with Cy3; Aqp11 and Stard5 BL6-specific probes labelled with Cy3, probes for the C7 allele labelled with Cy5; Prcp BL6-specific probes labelled with Cy5, probes for the C7 allele labelled with Cy3. Genes are listed in increasing order of number of SNV probes utilized, which is indicated for each gene. C. Probe properties for probe sets with high (>50%) and low (<50%) mean overall colocalization rate. We compared prevalence of individual nucleotides , nucleotides forming three hydrogen bonds (dC+dG) or two hydrogen bonds (dA+dT), purines (dA+dG) and pyrimidines (dC+dT) (middle row), as well as the number of folded structures predicted for each probe, mean and minimum folding energy for each probe (bottom row). For all plots, each spot represents the value obtained for a single probe.(TIF)Click here for additional data file.S5 FigA. The relationship between colocalization rate and correct assignment rate for RNAs in homozygous fibroblast cells. Each spot represents a measurement from a single cell and different dye combinations are displayed in different colors. B. The effect of colocalization rate on measurement accuracy. Each panel represents a single heterozygous fibroblast cell with >70% colocalization rate. The ratio of BL6 assignments measured in each cell is shown by the dashed line. The density plots show the allelic ratio after randomly downsampling the original RNAs to the indicated colocalization rate. C, D. Testing the effect of colocalization rate on “all-or-nothing” (C) and “coin flipping” (D) simulations. First, simulated cells were generated where the total number of RNAs was assigned an identity according to the model of interest. Next, the RNA in each cell was randomly downsampled to the number of RNAs that had been assigned a unique BL6 or JF1 identity in the original measurement. Each simulation was performed 5000 times. The simulated single-cell RNA counts from a randomly selected simulation are shown (top), as well as the correlations (middle) and log likelihoods (bottom) calculated for the entire population.(TIF)Click here for additional data file.S6 FigA. Measurements of allele-specific expression in bulk data for Aebp1, Lyplal1 and Mpp5 homozygous tissues. For each gene, allele-specific probes were either labelled with Cy3 for BL6 and Cy5 for JF1 (top row) or Cy5 for BL6 and Cy3 for JF1 (bottom row). Boxes indicate the dye combination used for subsequent experiments, and the pie charts shown in those boxes are the same as displayed in B. Single-cell allele-specific expression for Mpp5 in heterozygous tissue and allelic ratios detected in single-cell and bulk tissue data for heterozygous and homozygous tissues. The cells on the heterozygous scatter plot only contained integer numbers of RNA, but we included jitter to better display the density of cells with a given allelic distribution. The two colors on the heterozygous plots from the two reciprocal crosses (i.e. BL6 x JF1 and JF1 x BL6). C. Results of simulations for heterozygous Mpp5 data: for both “all-or-none” and “coin-flip” model we show simulated single-cell RNA counts (left) and negative log likelihood distribution of simulated and observed data (right).(TIF)Click here for additional data file.S7 FigA tissue scan of the Podxl guide probe  shows staining primarily in glomeruli, due to expression of the gene in podocytes (right). At 100x resolution these areas of high expression can clearly be distinguished from background when detecting fluorescence from the guide probe , as well as for the BL6  and JF1-specific probes . However, the high expression levels of the gene precludes the precise thresholding and detection of individual mRNA spots.(TIF)Click here for additional data file.S8 FigLog likelihood distributions of “all-or-none” (A) and “coin flipping” simulations (B). We show the negative log likelihoods calculated for 10,000 simulations for each gene, as well as the log likelihood of the real data (red line) using the same model as for the simulations.(TIF)Click here for additional data file.S9 FigEach spot represents the intensity of a single mRNA guide spot . Transcription sites were identified by overlap with intron probes labelled with Atto488 (replicate 1) or Alexa700 (replicate 2 and 3).(TIF)Click here for additional data file.S10 FigA, B. Probability of observed BL6 (A) and JF1 (B) mRNA counts given different burst sizes. Burst sizes boxed in red showed a good fit for the two alleles individually and were used for correlation analysis. C. Correlation of per-cell BL6 and JF1 mRNA counts given the burst sizes that showed a good fit (boxed in red in A and B). Burst sizes of the alleles are indicated, and for each correlation analysis the simulations for BL6 and JF1 mRNA counts were paired up randomly. In all figures bar plots represent simulations, red line represents real data.(TIF)Click here for additional data file.S11 FigAebp1 alleles is shown as grey bar plot, correlation for real data is indicated as red line.Correlation values for simulated counts in the case independent bursting from the two (TIF)Click here for additional data file.S1 TableXist was imaged using CalFluor610 (guide probe), Cy3 (BL6) and Cy5 (JF1) and allelic identity was determined using an automated pipeline as described in the Methods.(XLSX)Click here for additional data file.S2 TableFor each tissue we indicate which cluster size has the closest variance at a given subdivision size. A summary of this data is presented in (XLSX)Click here for additional data file.S3 TableUsing at least 10 randomly selected tiles we indicate what manual assignments we would have given the computationally identified Xist foci.(XLSX)Click here for additional data file.S4 TableWe show the name and genomic location of genes tested, the strains interrogated and the number of SNP probes used. We also indicate which final colocalization radius we selected, and what the overall colocalization rate as well the unique colocalization rate were at that radius.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(CSV)Click here for additional data file.S9 Table(CSV)Click here for additional data file.


Mycobacterium tuberculosis infection and autoimmune diseases like rheumatoid arthritis (RA) has been reported but it remains unclear if there is a causal relationship, and if so, which molecular pathways and regulatory mechanisms contribute to it. Here we used a computational biology approach by global gene expression meta-analysis to identify candidate genes and pathways that may link TB and RA. Data were collected from public expression databases such as NCBI GEO. Studies were selected that analyzed mRNA-expression in whole blood or blood cell populations in human case control studies at comparable conditions. Six TB and RA datasets  were included in the downstream analysis. This approach allowed the identification of deregulated genes that had not been identified in the single analysis of TB or RA patients and that were co-regulated in TB and RA patients compared to healthy subjects. The genes encoding TLR5, TNFSF10/TRAIL, PPP1R16B/TIMAP, SIAH1, PIK3IP1, and IL17RA were among the genes that were most significantly deregulated in TB and RA. Pathway enrichment analysis revealed ‘T cell receptor signaling pathway’, ‘Toll-like receptor signaling pathway,’ and ‘virus defense related pathways’ among the pathways most strongly associated with both diseases. The identification of a common gene signature and pathways substantiates the observation of an epidemiological association of TB and RA and provides clues on the mechanistic basis of this association. Newly identified genes may be a basis for future functional and epidemiological studies.Tuberculosis (TB) is among the leading causes of death by infectious diseases. An epidemiological association between  Mycobacterium tuberculosis (Mtb). With estimated 10.4 million active cases and 1.3 million TB-related deaths per year, TB is one of the most important infections worldwide ..23].After a thorough search and excluding datasets as specified above, two datasets for RA (GSE15573 and GSE4588) and 4 TB datasets  –27 were Normalization by Log2 transformation with autoscaling to each dataset was performed. Each dataset was then visually inspected using PCA plots to insure the absence of outliers. The individual analysis of each dataset was carried out using the Benjamini–Hochberg's False Discovery Rate (FDR)  with cutNetworkAnalyst was implemented to generate a protein-protein interaction (PPI) network by integrating the innateDB interactome database . With thIdentifying the overrepresented biological terms in the deregulated genes dataset was carried out using DAVID  . The anaTo detect the overrepresented transcription factor binding sites (TFBS) from DEGs, the transcription factor discovery module in NetworkAnalyst was implemented, and processing was carried out against both the JASPAR and ENCODE databases. To discover further possible connections between DEGs and transcription factors, we repeated the analysis using the EnrichR tool, which computes enrichment through 35 different gene-set libraries ,43. We dFrom the initial datasets acquired by searching public databases, six matched our predetermined inclusion criteria see . A detaiThe individual dataset gene expression normalization was carried out using the NetworkAnalyst log2 transformation function, followed by autoscaling. The individual datasets were inspected with PCA plots before and after normalization, and PCA plots of gene expression data of the 6 datasets before and after normalization are shown in Based on the results of Cochran’s Q test , the REMParticularly striking was the highly significant upregulation of the genes encoding Toll-like receptor 5 (TLR5)  and death receptor ligand TNFSF10/TRAIL , as well as the downregulation of protein phosphatase 1 regulatory subunit 16B PPP1R16B/TIMAP  and the E3 ubiquitin protein ligase 1 (SIAH1)  in both conditions. In addition, numerous immune response regulating genes were significantly deregulated only in the meta-analysis, including DDX58 and CLEC7A. A complete list of the differentially regulated genes with the highest significance is shown in To extract more biologically relevant information, we performed a network-based analysis. This analysis identified key hub genes among the most highly deregulated genes . MEPCE wPathway enrichment analysis was first performed using the KEGG based pathway enrichment identification module in NetworkAnalyst using the 341 genes identified as significantly deregulated . As expeTo visualize the enriched pathways and biological terms in a nonredundant manner we fed the identified DEGs to the ClueGO (35) tool on the Cytoscope program. This method identified cytokine signaling in immune response, viral translation, CD4-positive αβ-T cell differentiation, and innate immune response among the most significant terms. A network showing the most highly enriched pathways and terms from this analysis is shown in We searched for overrepresented transcription factor binding sites (TFBS) in the identified DEGs, using the TF exploration module in NetworkAnalyst. We scanned the regions around these DEGs with all core vertebrate transcription factor binding profiles present in the JASPAR and ENCODE databases. A network of transcription factors in the JASPAR analysis is shown in These results illustrate the usefulness of global gene expression meta-analysis to extract information that would not be visible in the analysis of individual datasets. Not only does it provide a higher sensitivity for the combination of independent sets of similar patient-cohorts, for instance, when several studies of a similar nature are analyzed together, but when combining datasets from different conditions this approach also opens the possibility of identifying gene sets that are similarly regulated in separate conditions. We here used this approach to identify DRGs, pathways, and regulatory networks that are shared between the two conditions of TB and RA.per se, but it supports functional studies and provides the basis for in-depth analyses.In this approach, 172 genes were found as differentially regulated in the disease cohorts only through the gene expression meta-analysis, besides many potentially shared pathways between RA and TB that can shed light on the mechanisms of TB triggered autoimmunity. Innate immunity pathways and antiviral defense pathways were identified as the most highly significantly enriched pathways. This may not be surprising One of the earliest events that can be detected in RA-development is an innate immune response and the activation of various antigen-presenting cells such as dendritic cells and macrophages ,45. The Key players in RA are B cells . B-cell In our analysis we also saw many genes that regulate cell death (necroptosis or pyroptosis) pathways, such as TRAIL , MLKL (mixed lineage kinase domain-like), EIF2AK2 and caspase-1/5 . The role of programmed cell death during Mtb-infection by necroptosis, pyroptosis or apoptosis is still somewhat controversial with various studies suggesting (for different cell types) an enhancement or attenuation of cell death during TB infection . A possiEnrichment in antiviral defense related pathways harboring various deferentially expressed genes such as EIF2AK2, DDX58, OAS1, CASP1, and GSK3B was also seen; these genes have been found to play a role in influenza, HPV, EBV and other viral response-related pathways. Indeed, it has previously been shown in a comparative gene expression profiling study between disease discordant twins with systemic autoimmune diseases including RA that antiviral pathways are consistently upregulated . While tUnderstanding the causative factors for autoimmune diseases such as RA remains a challenge, especially when studying it in a narrow context like the genetic role or the environmental role alone. The patient’s genetic background (such as the carriage of the HLA-DRB1*04 epitope cluster) is likely to play a role in predisposing patients to be more sensitive towards environmental insults such as infections . Our stuS1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.


Synapse clustering facilitates circuit integration, learning, and memory. Long-term potentiation (LTP) of mature neurons produces synapse enlargement balanced by fewer spines, raising the question of how clusters form despite this homeostatic regulation of total synaptic weight. Three-dimensional reconstruction from serial section electron microscopy (3DEM) revealed the shapes and distributions of smooth endoplasmic reticulum (SER) and polyribosomes, subcellular resources important for synapse enlargement and spine outgrowth. Compared to control stimulation, synapses were enlarged two hours after LTP on resource-rich spines containing polyribosomes (4% larger than control) or SER (15% larger). SER in spines shifted from a single tubule to complex spine apparatus after LTP. Negligible synapse enlargement (0.6%) occurred on resource-poor spines lacking SER and polyribosomes. Dendrites were divided into discrete synaptic clusters surrounded by asynaptic segments. Spine density was lowest in clusters having only resource-poor spines, especially following LTP. In contrast, resource-rich spines preserved neighboring resource-poor spines and formed larger clusters with elevated total synaptic weight following LTP. These clusters also had more shaft SER branches, which could sequester cargo locally to support synapse growth and spinogenesis. Thus, resources appear to be redistributed to synaptic clusters with LTP-related synapse enlargement while homeostatic regulation suppressed spine outgrowth in resource-poor synaptic clusters. LTP is a cellular mechanism of learning and memory known to alter dendritic spines and synapses. In the mature hippocampus, LTP-related synapse enlargement is balanced by stalling spinogenesis17. In contrast, LTP induced in the developing hippocampus enhances spinogenesis18. Live imaging of cultured neuronal dendrites reveals that when potentiation is restricted to one or a few spines, other spines further along the same dendrite shrink or disappear20. Individual axons that synapse on neighboring spines from the same dendrite produce clusters15. However, in many brain regions this axon connectivity-induced clustering is rare, yet spines still appear to form clusters along those dendrites as well11. Computational modeling shows that spine clusters can facilitate the spread of synaptic signals throughout the dendritic arbor, and depending on brain region and activation patterns can also facilitate the sharing of resources locally21. As such, the local events initiated in synaptic clusters could participate in synaptic tagging and capture of subsequent resources that sustain LTP24. Subcellular resources necessary to maintain LTP are limited and their redistribution to potentiated synapses could preserve spines in clusters undergoing LTP or learning13. Redistribution of resources away from non-potentiated synapses would provide a homeostatic mechanism to balance total synaptic weight, measured structurally as summed synaptic surface area along mature dendrites16. This homeostatic process is disrupted in many disease states26. Thus, it is important to learn how spine and synaptic clusters form and under what circumstances they are maintained.In the past decade, much work has revealed that clusters of dendritic spines form a computational unit of synaptic plasticity, learning, and memory28. SER regulates calcium both globally29 and locally and provides post-translational modification and trafficking of integral membrane proteins30. In dendritic shafts, local regions of SER complexity with high volume or branching retain cargo, such as ion channels and receptors, for enhanced delivery to nearby synapses via ER exit sites32. In dendritic spines, SER provides compartmentalized regulation of calcium during synaptic plasticity36. Some large spines contain a spine apparatus, in which folds or cisterns of SER are stacked around dense-staining plates that contain the actin binding protein synaptopodin40. Eliminating synaptopodin results in loss of the spine apparatus coincident with impaired LTP and learning42. Immunogold labeling of the spine apparatus reveals molecules involved in the synthesis and post-translational modification of proteins44. These features suggest that the spine apparatus is a small Golgi outpost, similar to the dynamic Golgi outposts in dendritic shafts45. Labeling and imaging of SER-specific molecules has revealed it to be a highly dynamic structure that moves in and out of dendritic spines, in developing, organotypic, and cultured neurons46. Similarly, synaptopodin protein has been used as a proxy to track plasticity of putative spine apparatuses in developing or cultured neurons48. Synaptopodin occurs in other subcellular structures and in developing dendrites prior to the formation of spines or the spine apparatus, suggesting it could also be a marker of dynamics unrelated to the spine apparatus49. Little is known about the dynamics of SER during synaptic plasticity in the mature brain30. The SER enters less than 13% of mature CA1 dendritic spines and forms a spine apparatus in less than 10% of these spines in vivo50. All of these characteristics indicate that three-dimensional reconstruction from serial section electron microscopy (3DEM) is required to determine the structure and location of SER and the spine apparatus.One resource that controls dendritic spine number and synapse size locally is smooth endoplasmic reticulum (SER). SER is a continuous membrane network extending from the soma throughout neuronal processes and into some dendritic spines51. Polyribosomes (which are readily identified through 3DEM)53 provide a conservative estimate of local translation because monosomes (which are not readily identified) are also capable of protein translation55. Polyribosomes undergo both rapid and sustained changes in frequency, demonstrating the dynamic state of local protein synthesis following LTP and learning56. However, the role of polyribosomes in local spine and synapse clustering is not known.Local protein synthesis is another resource that can control the outgrowth of dendritic spines and synapse enlargement during LTP and other forms of plasticityHere we used 3DEM to determine whether synaptic clusters are delimited by the availability of SER and polyribosomes in adult hippocampus. We tested whether spines with enlarged synapses collaborated or competed with their neighbors for resources to strengthen local synaptic input after LTP, relative to control stimulation in the same adult hippocampal slices.stratum radiatum of area CA1 to demonstrate input specificity of control and potentiated synapses  was delivered to one stimulating electrode at an intensity and pattern that produces multiple action potentials, engages multiple molecular mechanisms, and saturates LTP, thus maximizing the likelihood of observing long-lasting ultrastructural outcomes57. Both the control and TBS electrodes received the same total number of test pulses at a rate of 1 test pulse per 2 minutes throughout the experiment  along the surface of the dendrite membrane. The SER was identified as irregularly shaped, membranous cisternae with a clear lumen Fig. . Most deThese findings raised the question of whether spines with SER accommodated more synaptic growth simply because they were larger than the spines without SER. Spine head diameters did not differ significantly across the LTP and control conditions for spines with or without SER Fig. . HoweverAll spines containing a spine apparatus had head diameters greater than 0.38 µm , less on spines containing only polyribosomes (4%), and negligible on spines lacking both resources (0.6%) in the LTP condition relative to control stimulation. Resource-poor spines of comparable size to resource-rich spines showed less synapse enlargement (2%) with LTP. Thus, LTP-related synapse enlargement relied on local resources, not just spine size. Polyribosome frequency was highest in synaptic clusters with resource-rich spines under control conditions. After LTP, polyribosomes were reduced specifically in synaptic clusters that had a spine apparatus, suggesting these polyribosomes had been used for local translation during synapse enlargement. Total synaptic weight, measured as the summed PSD surface area per cluster length, was balanced across LTP and control conditions. Nevertheless, following LTP, resource-rich synaptic clusters had greater total synaptic weight than resource-poor clusters. These outcomes suggest that synapses on spines in resource-poor clusters were not available or ready for potentiation and, further, that these spines were susceptible to and maybe even targeted for removal after the induction of LTP16. Here we showed that this reduction in polyribosomes was specific to resource-rich synaptic clusters that had a spine apparatus. If local translation of mRNAs produced cytoplasmic proteins needed for synapse enlargement, then disassembly of polyribosomes could reflect completion of that process before 2 hours. In contrast, when LTP was induced by tetanic stimulation (3 times 1 second of 100 Hz), polyribosomes remained elevated in dendritic spines for 2 hours60. In that study, slices were equilibrated for about 1 hour prior to experimentation, as was commonly done in the past. Later we learned that at least 3 hours are required for spine recovery from slicing61. The data reported in the present study are from slices that had been equilibrated for at least 3 hours prior to experimentation. In the prior study, the ongoing recovery from slicing could have influenced the timing or magnitude of local protein synthesis and SER response to LTP61. In addition, the TBS used here produces many action potentials and engages multiple molecular mechanisms. Tetanic stimulation, on the other hand, produces only one or two action potentials, which could limit molecular mechanisms engaged, mRNA translation, or ribosomal disassembly54.Prior work showed that dendritic polyribosomes were elevated at 5 minutes after TBS but their abundance declined markedly by 2 hours66. Elevations in calcium can be localized within spines or spread through the dendritic shaft62, ultimately propagating to the nucleus where calcium transients influence gene transcription67. In the hippocampus, ryanodine receptors (RyR) localize to SER in dendritic spines68 and respond to calcium entering through ionotropic glutamate receptors and voltage-gated calcium channels62. RyR activation results in calcium-mediated calcium release that amplifies an otherwise weak signal72. Inositol trisphosphate receptors (IP3Rs), on the other hand, are localized to SER in the dendritic shaft68 and are activated by calcium and IP362. Hotspots of IP3Rs occur along SER in CA1 dendrites where SER is more elaborate. Furthermore, calcium released from SER via IP3R activation during LTP is involved in coordinating plasticity among synaptic sites along dendrites75. Thus, SER elaboration in and at the bases of spines during LTP could enhance local calcium signaling and serve as a potentiating signal to sustain and enlarge their synapses77. In contrast, where dendritic shaft SER comprises simple tubules, less calcium would be released and phosphatases would be more likely to be activated79, possibly leading to local spine loss.SER contributes to the regulation of calcium dynamics directly, or through its interaction with scaffolding and other proteins43, partly because rough endoplasmic reticulum could not be identified unequivocally in this material. However, since the spine apparatus has immuno-reactive markers for proteins specific to the Golgi apparatus, it might provide post-translational modification of membrane proteins in spines following LTP81. Synaptopodin is an essential component of the mature spine apparatus45. Live-imaging of cultured neurons shows spines containing synaptopodin have larger AMPA receptor-mediated responses from RyR-triggered calcium release47. Two-photon microscopy reveals synaptic depression is also regulated by IP3-dependent mobilization of calcium at large spines associated with synaptopodin33. Thus, the LTP-related elaboration of the spine apparatus in mature dendritic spines could enhance both the regulation of intra-spine calcium and the post-translational modification and trafficking of proteins at enlarged spine synapses.Whether the spine apparatus formed in response to a local burst in membrane-associated protein synthesis during LTP remains to be determined31. In perfusion-fixed hippocampus, shaft SER volume and branching was greater in portions of the dendrite with more or larger dendritic spines31. Similarly, under the control conditions reported here, shaft SER volume and branching was greatest in synaptic clusters where total synaptic input was highest. These relationships were retained 2 hours after LTP in synaptic clusters that had resource-rich spines. However, shaft SER branching was markedly decreased in asynaptic regions as well as in synaptic clusters lacking resource-rich spines after LTP. These findings suggest that shaft SER membrane was redirected from asynaptic regions and clusters with low spine density to synaptic clusters where spines were preserved, and synapses were enlarged. Several molecular mechanisms could be triggered that would link LTP to elaboration of the spine apparatus and local reorganization of dendritic shaft SER. Protein kinase C-mediated phosphorylation of CLIMP-63, an integral membrane protein of SER, causes SER to dissociate from microtubules and become more elaborate82. CLIMP-63 could also be phosphorylated by other products of LTP such as Casein kinase II (CKII)84. Further along the dendrite, away from activated spines, the dephosphorylation of CLIMP-63 causes SER to associate with microtubules and become more tubular82. This simplification of SER would enhance movement of proteins and other cargo away from less active synaptic clusters, thereby stalling the formation of new spines or leading to the elimination of weak spines16.Local delivery of SER cargo is greatest where SER structure is most complex, because increased geometric complexity results in slower trafficking of membrane and proteins and forms ER exit sites20, or release from cadmium block of extracellular calcium19, at multiple spines on cultured hippocampal neurons produced LTP, and resulted in shrinkage or loss of one or a few spines elsewhere along the dendrite. Adult CA1 dendrites in our acute slices have a greater spine density (3–4 spines/µm of the full segment including both synaptic and asynaptic regions)16 than CA1 dendrites in the dissociated hippocampal cultures (~1 spine/µm)20. In the present configuration, TBS should activate most if not all axons in the field. Hence, it is not surprising that these preparation- and paradigm-related differences alter the magnitude of homeostatic balance in spine density after LTP. Prior work also showed that 3 hours of recovery plus 3 hours of control stimulation were needed to return spine density in hippocampal acute slices up to in vivo levels for both P15 and adult ages (P55–75)17. Nevertheless, even following the requisite slice recovery (and same TBS induction paradigm) contrasting outcomes resulted between these ages, with spinogenesis at P15 but synapse enlargement and compensatory spine reduction in adults18. The total number of test pulses was the same in both ages and the control stimulation and TBS were delivered in the same slice. Therefore, the effects of age on LTP-related synaptic plasticity were not due to differences in paradigms. Instead the age-related differences in response to LTP likely reflect different stages in synaptogenesis, where P15 dendrites have reached less than 50% of the adult spine density85.How these results will generalize to other developmental stages, plasticity paradigms, or brain regions remains to be determined. Glutamate uncaging15. However, axonal branches from the same neuron do not form spine clusters on most of the target dendrites, especially in the more proximal CA1 dendritic locations studied here15. In fact, same dendrite/same axon pairing between synapses is quite rare in the middle of stratum radiatum89. Yet, as shown here, spine clustering is a highly dynamic process along CA1 stratum radiatum dendrites. We conclude that the resource-rich spines were ready to express lasting LTP by enlarging their own synapses and conferring stability and growth to their neighbors, resulting in robust synaptic spine clusters. The local dendritic resources of SER, polyribosomes, and molecular products of synaptic potentiation  could then tag the entire cluster for growth, preservation, and subsequent plasticity59. Future work is needed to determine whether the formation and stabilization of distal spine clusters are also supported by postsynaptic resources.Recent findings show that synapse clustering on dendrites can be driven by a single presynaptic axon, which overcomes the electrotonic disadvantage of being located in the distal dendritic tuft of hippocampal CA1 pyramidal cells16. Adult male Long-Evans rats aged 60–61 days old (319–323 g) were anesthetized with halothane and decapitated. Hippocampal slices 400 µm thick were collected from the middle third of the hippocampus  and recovered in an interface chamber in artificial cerebrospinal fluid  for ~3 hours at 32 °C  for ~30 minutes. Theta-burst stimulation  was delivered to one stimulating electrode at time 0 to produce LTP  and microwaving the slice in fixative for 10 sec. Slices were kept in fixative overnight at room temperature and then embedded in agarose and vibra-sliced at 70 µm . For each stimulation site, the vibra-slice containing the electrode indentation along with two adjacent vibra-slices were processed for EM through a 1% osmium/1.5% potassium ferrocyanide mixture, 1% osmium alone, dehydrated through graded ethanols (50–100%) and propylene oxide, embedded in LX112, and placed in a 60 °C oven for 48 hours4. Three samples were obtained from each experiment spanning the regions beside and beneath the stimulating electrodes. The absolute positions of the 3DEM samples were located ~120 µm away from the stimulating electrode tips, at a depth of ~100 µm from the slice air surface  and counterstained with ethanolic uranyl acetate and Reynolds lead citrate. The serial sections and a calibration grid (Ted Pella Inc.) were then imaged on a JEOL 1230 transmission electron microscope  with a Gatan digital camera.90. All dendrites that spanned the central section of the series were numbered and viewed through serial sections to ascertain their orientation and caliber by measuring the diameter and counting microtubule numbers on sections near the beginning, middle, and end of the series. To be included, dendrites needed to be cross-sectioned for most of their length, as it is difficult to identify spine origins and SER when dendrites are longitudinally sectioned. In addition, the dendrites had to have similar calibers such that spine density in the control condition was not correlated with microtubule number , and aligned. Section thickness was computed using the cylindrical diameters method: dividing the diameters of longitudinally sectioned mitochondria by the number of sections the mitochondria spannedber Fig. . A totalTo identify points where SER branched, we wrote a simple script in Python that treated individual SER traces created on each section in Reconstruct as vertices. Edges between vertices were identified when the traces overlapped one another on adjacent serial sections. For a trace, the number of SER branching events between two EM sections, b(v), was defined as b(v) = max(deg(v) – 2, 0), where deg(v) is the degree of the vertex v, or the number of edges to which the vertex belonged. Non-branching SER traces existed when the vertex had zero, one, or two neighboring traces on adjacent sections (for deg(v) = 0, 1 or 2) and b(v) was zero for each of these cases to avoid negative numbers. In other words, zero was a disconnected piece of SER, one was the end of a tubule, and two was the middle of a tubule. A vertex with three edges had a single branch, and so on. This metric proceeded linearly such that all SER segments were followed through adjacent serial sections and the number of branch points summed on each section Fig. .10 transformed for the reported statistical analyses. Hierarchical nested analyses of variance (hnANOVAs) were performed when many measures  were obtained from individual dendrites . When just one or a few measures were obtained from a dendrite  then full-factorial or one-way ANOVAs were performed controlling for experiment, and the F values with subscripted degrees of freedom (df) presented for condition and error in the figure legends with absolute p values . Levene’s test for homogeneity of variance was performed to assure validity of the ANOVA. Analyses of covariance (ANCOVA) were performed while controlling for head diameter  were computed (Sum of Squares (SS) factor/(SS factor +SS error)) depending on the specific factor  as in the text or legends. In the text, effect sizes are reported as a percentage based on the η2 ratio multiplied by 100%.Statistical analyses were performed in STATISTICA . Chi-squared test of independence was used to investigate changes in the proportions of spines with different SER content. The distributions of synapse areas and spine head diameters were significantly skewed; hence, these data were logter Figs  and 3C–Fter Figs . Post-ho


The ME-TMA was stained by brightfield and fluorescence multiplex immunohistochemistry (IHC) to evaluate expression levels of TIGIT and PD-1 as well as standard lymphocyte markers  in the lymphocytic background. All analyzed cases of HL contained 9–99% (median: 86%) of TIGIT+ lymphoid cells. In general, TIGIT localized to the same cells as PD-1. Strikingly, expression levels of TIGIT and PD-1 were highly variable among the analyzed samples. Highest levels of TIGIT and PD-1 were found in one sample of nodular lymphocytic-predominant HL (NLPHL). In conclusion, TIGIT expression is highly variable between patients with Hodgkin’s lymphoma. Our results encourage further studies evaluating the role of TIGIT as a target for immunotherapies in Hodgkin’s lymphoma.Hodgkin’s lymphoma (HL) is characterized by a high background of inflammatory cells which play an important role for the pathogenesis of the disease. T cell immunoreceptor with Ig and ITIM domains (TIGIT) is an inhibitory immune checkpoint receptor and a putative target for novel immunotherapies. To study patterns of TIGIT expression in the T cell background surrounding malignant cells including Hodgkin cells, Reed-Sternberg cells and histiocytic cells, a microenvironment (ME) tissue microarray (TMA) was constructed from tissue punches measuring 2 mm in diameter obtained from formalin-fixed tissue samples of Hodgkin’s lymphoma lymph nodes (The online version of this article (10.1186/s12885-018-5111-1) contains supplementary material, which is available to authorized users. Hodgkin’s lymphoma (HL) is a malignant transformation of B cell origin that accounts for about 20–30% of lymphomas in Western societies –3. AlthoT cell immunoglobulin and ITIM domain (TIGIT), a co-inhibitory transmembrane glycoprotein of the poliovirus receptor (PVR)/−nectin superfamily, is another interesting candidate for novel checkpoint therapies , 9. UsinAs to yet, data on the possible role of TIGIT in Hodgkin’s lymphoma are lacking. Here, we made use of a microenvironment (ME) tissue microarray (TMA) that was constructed from 2 mm tissue punches each from 40 Hodgkin’s lymphomas and studied patterns of TIGIT and PD-1 expression by means of conventional bright field and multiplex fluorescence immunohistochemistry.Formalin-fixed paraffin-embedded tissue samples from 40 patients with Hodgkin’s lymphoma were selected from the archives of the institute of Pathology of the University Medical Center Hamburg-Eppendorf, Germany. The selection included 30 patients diagnosed with nodular sclerosis classical HL (NSCHL), 7 patients with mixed cellularity classical HL (MCCHL), one patient with lymphocyte rich classical HL (LRCHL), and one patient with nodular lymphocyte predominance HL (NLPHL). The histological subtype was undetermined for another patient.A pathologist reviewed all cases and selected areas containing Hodgkin and Reed-Sternberg cells for TMA construction. A single tissue punch measuring 2 mm in diameter was taken from each donor tissue block to capture the lymphocytic background adjacent to the malignant cells. In addition, two punches of normal human tonsil were added to the TMA as a reference tissue for fluorescence measurement normalization. The usage of archived diagnostic left-over tissues for manufacturing of tissue microarrays and their analysis for research purposes as well as patient data analysis has been approved by local laws  and by the local ethics committee .Freshly cut 4 μm tissue sections were used for immunohistochemistry (IHC) analyses. For brightfield IHC, tissue sections were dewaxed and incubated in an autoclave for 5 min at 121 °C in Tris-EDTA pH 7.8 antigen retrieval solution prior to blocking of endogenous peroxidase and incubation of the primary antibody . Bound antibody was detected with the DAB-kit  and slides were counterstained and sealed in EUKITT®. For fluorescence multiplex IHC, the Opal™ dye kit  was used. The experimental procedure was performed according to the manufacturer’s instructions. Slides were initially boiled in a microwave oven for 15 min at 100 °C for antigen retrieval. Three different primary antibodies were combined with DAPI staining in each experiment. One circle of antibody staining included peroxidase blocking, application of the primary antibody, detection with a secondary HRP-conjugated antibody, fluorescence dye detection, and removal of the bound antibodies by microwave treatment (15 min at 100 °C). This cycle was repeated two times for the remaining antibodies. Slides were subsequently counterstained with diamidino-2-phenylindole (DAPI) and mounted in antifade solution. Details on the used antibodies, antibody retrieval procedures and Opal™ dyes are given in Table Digital images of multiplex immunofluorescence stainings were acquired using a Leica Aperio VERSA 8 automated epifluorescence microscope. For initial image acquisition, exposure time was manually adjusted for each fluorochrome to minimize auto fluorescence. Subsequently, a threshold for positive staining was defined as follows: The fluorescence intensity of each antibody was measured in 50 to 200 cells of a cell type with known lack of expression and the fluorescence value of the cell with highest “false positive” measurement was used to define the cutoff value for positive expression.2). Tissue areas for analysis were manually defined and typically included between 18,000 and 30,000 cells. Fluorescence intensity values were recorded for each fluorochrome in each cell. The average fluorescence intensity of all cells of the same compartment from all measured areas was calculated and used for comparisons. Because staining intensity largely depends on antibody specification and staining protocols we sought to introduce a reference tissue for staining normalization to our experiments. We selected two samples of tonsil tissue, which were placed on the same TMA along with the study samples. In our previous study [After slide scanning, image analysis was performed using Image Scope software package . Image analysis included segmentation of individual cells and subsequent measurement of the fluorescence intensity  analysis of variance (ANOVA) test was employed to calculate compartment specific expression differences of PD-1 and TIGIT in tissue samples.All 40 HL included in this study were successfully analyzed and showed detectable TIGIT staining in 9–99% (Median: 86%) of the lymphocytic background cells by means of conventional bright field immunohistochemistry. Representative images are shown in Fig. We performed multiplex fluorescence immunohistochemistry to unravel the immune cell types expressing TIGIT and to quantify TIGIT expression among individual patients. TIGIT was virtually undetectable on CD20 positive B lymphocytes but expressed on a large fraction (70%) of CD3 positive T lymphocytes. Further analysis revealed that TIGIT positive T cell subtypes included CD8 cytotoxic T cell, CD4 helper T cells and FOXP3 regulatory T cells . On average across all patients, it showed that the majority (68%) of T cells had co-expression of TIGIT and PD-1, while isolated positivity for TIGIT and PD-1 was seen in only 14 and 5%. The remaining 13% T cells had neither TIGIT nor PD-1 expression  of TIGIT positive T cells also expressed PD-1 in our study. In normal lymph nodes and tonsils, TIGIT and PD-1 co-expression was found in 50–99% of T cells depending on the analyzed T cell subtype . These f2 [In contrast to many other studies that reported PD-1 staining in 20–60% of classical HL –27, we d2  had dete2 .The fluorescence approach enabled us to quantify the expression level of TIGIT and PD-1. We have recently shown that highest physiological TIGIT and PD-1 expression was found in follicular T helper cells residing at the periphery of germinal centers in lymph nodes and tonsils . Given tNotably, our series of HL included one case of nodular lymphocyte predominant Hodgkin’s lymphoma (NLPHL). A unique feature of NLPHL is that the lymphocytic predominant (LP) cells (previously known as “popcorn” cells) arise in a follicular like microenvironment that shares characteristics of germinal centers in lymph nodes and tonsils, such as a high density of follicular T helper cells , 33. ThaAnother feature of HL is that the tumor cells have developed mechanisms to escape the excessive immune-cell infiltration of the host. For example, the cellular landscape of HL is characterized by a paucity of specific cytotoxic T cells and natural killer (NK) cells . Much hoThe large tissue size TMA (ME-TMAs) enable the analysis for large enough tissue areas to study a tumor’s microenvironment under perfectly standardized experimental conditions. Having all tissue samples of a study on one glass slide ensures not only fully identical staining procedures for every tissue specimen but also identical section thickness and slide age . This isIn summary, the results of our study identify HL as a tumor type with frequent but variable expression of immune checkpoint receptors such as TIGIT and PD-1. Patients with HL might benefit from potential future therapies targeting TIGIT alone or in combination with other drugs.Additional file 1:Figure S1. Serial dilution of the TIGIT antibody in lymph node and a NLPHL. (PDF 6352 kb)


To explore the utility of diffusion and perfusion changes in primary renal cell carcinoma (RCC) after stereotactic ablative body radiotherapy (SABR) as an early biomarker of treatment response, using diffusion weighted (DWI) and dynamic contrast enhanced (DCE) MRI.Patients enrolled in a prospective pilot clinical trial received SABR for primary RCC, and had DWI and DCE MRI scheduled at baseline, 14 days and 70 days after SABR. Tumours <5cm diameter received a single fraction of 26 Gy and larger tumours received three fractions of 14 Gy. Apparent diffusion coefficient (ADC) maps were computed from DWI data and parametric and pharmacokinetic maps were fitted to the DCE data. Tumour volumes were contoured and statistics extracted. Spearman’s rank correlation coefficients were computed between MRI parameter changes versus the percentage tumour volume change from CT at 6, 12 and 24 months and the last follow-up relative to baseline CT.Twelve patients were eligible for DWI analysis, and a subset of ten patients for DCE MRI analysis. DCE MRI from the second follow-up MRI scan showed correlations between the change in percentage voxels with washout contrast enhancement behaviour and the change in tumour volume . The change in mean initial rate of enhancement and mean Ktrans at the second follow-up MRI scan were positively correlated with percent tumour volume change at the 12 month CT onwards . Changes in ADC kurtosis from histogram analysis at the first follow-up MRI scan also showed positive correlations with the percentage tumour volume change , but these results are possibly confounded by inflammation.DWI and DCE MRI parameters show potential as early response biomarkers after SABR for primary RCC. Further prospective validation using larger patient cohorts is warranted. Recentlelivered ,5.It is known that SABR of RCC can achieve high rates of local control, however, ablative doses of radiotherapy can lead to cell-kill and cancer control without necessarily resulting in tumour morphological change, and residual non-viable tumour architecture can remain for sustained periods post-treatment . DespiteMRI offers an alternate means to evaluate tumour response  and is cIn this study we explored the utility of diffusion and perfusion changes shown in primary RCC tumours after SABR as an early biomarker of treatment response using DWI and DCE MRI.This was a single institutional prospective clinical trial with ethics review board approval at the Peter MacCallum Cancer Centre. Patients with primary RCC who were not operative candidates received SABR and underwent MRI scanning with Gadolinium contrast enhancement unless having medical contraindications . A schematic of scheduled MRI and CT imaging time points and SABR treatment delivery time is shown in Radiotherapy fractionation was determined based on tumour size. For primary RCC with a tumour size of ≤5 cm diameter, a single fraction of 26 Gy was prescribed, and for larger tumours three fractions of 14 Gy was prescribed to the 99% of the target volume. The clinical target volume (CTV) was defined using an internal target volume with no margin for microscopic disease, as defined by the tumour delineated from 10 respiratory sorted bins on a 4D CT. The planning target volume (PTV) was defined by applying a 5 mm isotropic expansion to the CTV. Three-fraction treatment was delivered on non-consecutive days over one week. Treatment was delivered using a 3D conformal technique on a conventional linear accelerator system as previously described .3 from each CT scan we assumed the tumour was an ellipsoid shape according to the following equation [Tumour measurements were obtained on CT before SABR treatment (baseline), and three monthly for the first year at 6, 9 and 12 months. After this, CT scans were taken at the treating clinician’s discretion, ranging from 4 to 6-monthly until the trial was closed. No other tumour directed therapy was delivered in the interval. In this study, we analysed the scheduled 6 month, 12 month, 24 month and the last follow-up CT, where the last follow-up CT ranged between 12 months to 36 months after SABR treatment  and at two follow up time points scheduled for 14 days and 70 days after SABR . Images 2, acquisition matrix = 128 x 128, FOV = ranging from 360 x 360 mm to 420 x 420 mm, with 18–24 transverse slices with thickness = 4mm and spacing between slices = 6 mm. ADC maps were generated using a mono-exponential fit to the DWI data in Siemens software.Diffusion weighted images (DWI) were collected using an axial echo-planar imaging sequence with b values of 50, 400 and 800 s/mmDCE MRI data was obtained using a 3D T1-weighted TWIST sequence and injecting each patient with a 10ml bolus injection of contrast agent Dotarem , followed by a saline flush. The dynamic scans were acquired in the axial plane with acquisition matrix = 192 x 192, FOV = 400 x 400 mm, flip angle = 20° with 20 transverse partitions at 3.6 mm slice thickness and repeated 30 times at 5.2 second intervals. This time period was the same for all scans except the pre-treatment scan for patient 2 who had images repeated 50 times at the same interval of 5.2 seconds. The dynamic scan was preceded by a pre-contrast TWIST sequence with variable flip angles  to enable calculation of a T1 map, however this data was not utilised (see below).Changes in water diffusivity within each tumour after SABR was assessed by analysing the ADC maps computed from DWI data. ADC maps were read into 3D Slicer software  and the onset), the initial rate of enhancement (IRE), the maximum enhancement (ME), time of washout of the contrast agent (Twashout), the initial rate of washout (IRW). Additionally, each voxel’s contrast agent uptake curve was classified into one of four behaviours to give a Gadolinium (GD) map. The uptake curve behaviours included: (1) persistent enhancement where a voxel had increased in intensity however a maximum enhancement had not been reached, (2) plateau enhancement where there was both an increase in signal intensity and a plateau phase, (3) washout enhancement where an increase, plateau and a decrease in signal intensity was observed and (4) no enhancement where there was negligible change to the signal in a voxel over time   was used-1s-1 [Pharmacokinetic models were fitted to the DCE MRI data using Dynamika software   in order-1s-1 , and the-1s-1 . The mot-1s-1 . In addiTumour volumes were contoured by an experienced radiologist (BP) on the reference 3D TWIST images used for 2D motion correction, using visual correlation to the tumour margins as shown on the T2-weighted and post contrast MR images. Mean perfusion statistics were extracted from all semi-quantitative parametric and pharmacokinetic maps. Histogram analysis has shown limited utility for DCE data of primary RCC , so for The changes in mean ADC values and mean DCE parameters were calculated for each patient’s post treatment MR images relative to baseline MRI. Spearman rank correlation coefficients were calculated in R statistical software  to compaCharacteristics for the twelve patients eligible for analysis are detailed in An increase in mean and median ADC after SABR treatment was observed in eight patients at the second follow-up MRI scan relative to baseline while seven of these also had an increase in mean and median ADC at the first follow-up MRI scan relative to baseline . In addition, the change in ADC skewness at the first follow-up MRI scan was negatively correlated with the percentage change in tumour volume from the 12 month CT scan onwards, but was only significant at the 24 month CT time point . At the second follow-up MRI scan the change in ADC kurtosis showed a positive correlation from the 12 month CT onwards however this correlation and all other ADC parameter correlations were considered statistically not significant. The raw ADC kurtosis values for each MRI scan can be seen in Figure B in A significant positive correlation was found at the second follow-up MRI scan between the change in percentage tumour voxels having washout contrast enhancement behaviour and the change in tumour volume at the 12 month CT onwards . Graphs in The change in mean IRE at the second follow-up MRI scan was positively correlated with change in tumour volume from the 12 month CT scan onwards  as shown in Standard response assessments using morphological measurements and CT based contrast enhancement criteria after SABR for primary RCC are known to be challenging. In a recent study, it was shown that tumour growth rate and tumour size were reduced in primary RCC after SABR, however no significant differences in tumour signal enhancement were shown on multiphasic contrast-enhanced MRI during the initial post treatment period . The autAcquiring DWI and DCE MRI of the kidneys to assess their potential as biomarkers for primary RCC, however, poses a number of challenges ,15. CurrWithin this challenging context, we sought to carry out an exploratory analysis of diffusion and perfusion changes shown by DWI and DCE MRI after SABR for primary RCC and assess whether any correlate with treatment response shown on CT. We have carried out this assessment on patients with a consistent imaging protocol, acquired on the same MR scanner which has eliminated sequence and scanner variability. We have correlated changes in MRI parameters with percentage tumour volume change from consistent CT time points, while also assessing the last follow-up CT scan which we considered suitable as tumour shrinkage over time is typically slow and relatively linear . This laRadiation is known to induce inflammation in treated tissues, and is a known confounder when assessing response in patients who have undergone SABR for lung cancer . To our Our patient cohort showed restricted diffusion in their tumours on baseline ADC maps from DWI, which is consistent with a meta-analysis for focal renal lesions where ADC values were significantly lower than in benign tissue . Recent 2, are highly influenced by perfusion effects [It is important to consider that ADC was computed using a simplistic mono-exponential decay curve to the DWI data, which assumes diffusion obeys a Gaussian distribution. We know that DWI provides complex mixed information about cellularity and perfusion where low b-values, specifically those less than 200 s/mm effects . To extr effects  could be effects .High perfusion is a characteristic of tumours due to their abnormal microvasculature, which is reflected by higher signal intensities on DCE MRI. Correlation analysis showed a strong association at the second follow up scan between the change in percentage voxels showing washout contrast enhancement behaviour, which is typical of tumours, and the change in tumour volume at the 12 month CT time point onwards. Furthermore the change in IRE and in Ktrans, which are both dependent on local tissue permeability and perfusion, at this second scan were positively correlated with the percentage change in tumour volume from the 12 month CT scan onwards. With these findings, we hypothesise that patients who experienced tumour growth despite having SABR, still had viable tumour vasculature with angiogenic activity after treatment while patients whose tumours reduced in size likely had successful ablation of the associated perfusing vessels. There was a large amount of tumour tissue with no contrast enhancement located at sites of necrosis, and also in regions affected by motion which impeded data fitting. Methods ensured however, that voxels with no contrast enhancement were not included in the computation of mean DCE parameters so as not to skew the results.Results here are comparable with those shown in a number of oncological studies investigating the use of DCE MRI for treatment response evaluation after radiotherapy. In a study by Huang et al.  DCE-inteThere were limitations to our study, most notably the small number of patients and the large number of parameters assessed. Hence this study is necessarily of an exploratory nature, and we did not adjust for multiple comparisons. Motion of the kidney during DWI and DCE MRI data acquisition caused mainly by respiration provided challenges to the data analysis. The images were obtained during free-breathing which meant the kidney movement occurred predominantly in the coronal plane , which wWe assessed MRI tumour volumes using contours from one observer and extracted mean values for a number of MRI parameters which can be heavily influenced by outliers. Whilst we did investigate tumour contouring on the T2-weighted and post contrast MR images where tumour boundaries could be more easily seen, it was challenging to accurately co-register these images and the contour data to the lower resolution ADC maps and DCE MRI which had been more influenced by motion. Instead by contouring the ADC maps and DCE MRI data the radiologist could visually take the effect of motion into account. It will be a subject of future studies to investigate the impact of contour variation, particularly inter-observer variability. Future studies will also be done into assessing response categorisation using imaging data directly which can then be compared with conventional methods including RECIST, similar to the REMAP study which is assessing treatment response to antiangiogenic therapy for metastatic RCC using combined PET/MRI .Lastly, the time between imaging studies and treatment were inconsistent between patients . BaselinTo our knowledge, we have carried out the first exploratory study into assessing whether DWI and DCE MRI parameters could provide quantitative early functional imaging biomarkers for SABR treatment of primary RCC. Results showed that parameters from DCE MRI had strong potential as early response biomarkers, with the washout contrast enhancement curve behaviour at 70 days post SABR significantly correlating with the percentage change in tumour volume shown on CT at 12 months and onwards. In addition, the IRE and Ktrans parameters from DCE-MRI at 70 days post SABR showed potential utility for predicting treatment response at 12 months and onwards. ADC kurtosis from DWI at 15 days post SABR was significantly correlated with percentage tumour volume change at the 12 month CT time point however it is possible this scan was confounded by the effects of radiation-induced inflammation. Further prospective studies to assess and validate these potential biomarkers using larger patient cohorts are warranted.S1 FigSchematics showing (a) the piecewise linear function fitted to each voxel in the DCE MRI data after normalising the signal intensity to the average baseline signal intensity, from which parameters are extracted including Tonset = time of onset of the contrast agent, IRE = initial rate of enhancement, ME = maximum enhancement, Twashout = time of washout of the contrast agent and IRW = initial rate of washout; and (b) the contrast enhancement behaviour of each voxel is then categorised into either plateau, persistent, washout or no enhancement, to give a Gadolinium (GD) map. Implemented in Dynamika software  .(TIF)Click here for additional data file.S2 Fig(a) Mean ADC and (b) ADC kurtosis for each MRI scan for each patient. ADC kurtosis is dimensionless.(TIF)Click here for additional data file.S3 FigScatterplots showing the change in (a) mean ADC, (b) median ADC (c) ADC kurtosis and (d) ADC skewness from follow-up MRI scan 1 (left) and follow-up MRI scan 2 (right) versus percentage tumour volume change from CT.(TIF)Click here for additional data file.S4 FigScatterplots showing the change in (a) ME, (b) IRE and (c) IRW from follow-up MRI scan 1 (left) and follow-up MRI scan 2 (right) versus percentage tumour volume change from CT.(TIF)Click here for additional data file.S5 FigScatterplots showing the change in (a) Tonset, (b) Twashout, (c) Ktrans and (d) iAUGC60 from follow-up MRI scan 1 (left) and follow-up MRI scan 2 (right) versus percentage tumour volume change from CT.(TIF)Click here for additional data file.S6 FigScatterplots showing the change in percentage (a) plateau, (b) persistent (c) washout and (d) non-enhancing voxels from follow-up MRI scan 1 (left) and follow-up MRI scan 2 (right) versus percentage tumour volume change from CT.(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.