Spatio-temporal dynamics of macroglial cell organisation and proximity to blood vessels during postnatal development

Spatio-temporal dynamics of macroglial cell organisation and proximity to blood vessels during postnatal development | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Spatio-temporal dynamics of macroglial cell organisation and proximity to blood vessels during postnatal development Naomie Guille , Héloïse Monnet , Tristan Hourcade , Philippe Mailly , Martine Cohen-Salmon , View ORCID Profile Anne-Cécile Boulay doi: https://doi.org/10.1101/2025.01.14.632893 Naomie Guille 1 Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université Paris Sciences et Lettres (PSL) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Héloïse Monnet 1 Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université Paris Sciences et Lettres (PSL) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tristan Hourcade 1 Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université Paris Sciences et Lettres (PSL) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philippe Mailly 1 Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université Paris Sciences et Lettres (PSL) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martine Cohen-Salmon 1 Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université Paris Sciences et Lettres (PSL) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anne-Cécile Boulay 1 Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université Paris Sciences et Lettres (PSL) , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anne-Cécile Boulay For correspondence: anne-cecile.boulay{at}inserm.fr Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Brain cortical development results from the proliferation, differentiation, migration and maturation of many cell types. While neuronal development is well characterized, the mechanisms regulating macroglial cells (oligodendrocytes and astrocytes) development remain largely unknown. Recent works suggest that the vascular system plays a key, yet under-evaluated role in this process. To investigate this, we developed VeCell , a Fiji plugin designed to analyse the spatial organization of macroglial cells relative to blood vessels. Using immunolabeling for Sry-box transcription factor (Sox) 9 (macroglial progenitors and astrocytes) and Sox10 (oligodendrocyte lineage), we determined macroglia density, distribution and proximity to blood vessels from postnatal day (P) 1 to P60 in the somatosensory cortex. We showed that Sox9+ cells were evenly distributed across cortical layers with regular intercellular spacing. In contrast, Sox10+ cells concentrated in deeper cortical layers, and exhibited a random distribution. Vascular density and branching increased markedly between P5 and P15 and macroglial cells were closer to blood vessels from P15 onward. As a proof of concept, we used Vecell to show that astrocyte cortical distribution is preserved in MLC1-deficient mice, a model of Megalencephalic leukoencephalopathy with subcortical cysts, in which astrocyte perivascular coverage is altered. Thus, VeCell is a powerful tool to characterize and quantify macroglial cell distribution in the brain and in relation to the vasculature. It revealed distinct distribution and postnatal development patterns for astrocytes and oligodendrocytes. Table of Contents Entry VeCell: quantification of macroglial cell density, distribution, and proximity to blood vessels From P5, Sox9⁺ cells are evenly distributed, unlike Sox10⁺ cells From P15, Sox9⁺ cells are located closer to blood vessels than Sox10⁺ cells Download figure Open in new tab Introduction The mammalian cortex is a complex and highly organized tissue which contains diverse neuronal, glial and vascular cell types. Cortical development is driven by highly regulated and coordinated sequences of cell proliferation, differentiation and migration, and requires the fine tuning of production/elimination and attraction/repulsion mechanisms. Neurons and macroglial cells - astrocytes and oligodendrocytes - are generated from common multipotent neural precursor cells (NPCs) ( Miller and Gauthier, 2007 ). After neuron production, which starts around embryonic day (E) 12 and peaks at E15, NPCs switch to gliogenesis around E18 to sequentially generate astrocyte progenitors and oligodendrocyte precursor cells (OPC). Cortical astrocytes are produced mainly during the first postnatal week from a combination of delaminating NPCs and local proliferation of astrocyte progenitors following colonization of the cortical parenchyma ( Clavreul et al., 2019 ; Ge et al., 2012 ). From the second postnatal week, their migration is halted and astrocytes mature both molecularly and morphologically to finally tile the brain with non-overlapping domains ( Bushong et al., 2004 , 2002 ; Halassa et al., 2007 ). Oligodendrocyte lineage cells (OligoLC) comprise both oligodendrocyte precursors (OPCs) and myelinating oligodendrocytes ( Foerster et al., 2024 ). During cortical development, OPCs are produced in three spatiotemporal waves. In the postnatal cortex, OPCs generated during the first embryonic wave undergo massive cell death. They are eliminated from the cerebral cortex by P10 and progressively replaced by OPCs locally-produced from cortical progenitors ( Kessaris et al., 2006 ). After birth, OPCs progressively differentiate into oligodendrocytes (OLs) that form the myelin sheath around axons, mostly before the end of the 3 rd postnatal week ( Orduz et al., 2019 ), some NG2 + OPCs remaining in the mature brain. Brain vascular network formation is initiated earlier than macroglial cells. Around embryonic day (E)9, the primary vascular network starts to form. From a perineural plexus, vascular sprouts invade the neuroectoderm by vasculogenesis and angiogenesis ( Engelhardt, 2003 ). Postnatally, high angiogenesis rate results in an extensive vascular expansion between P8 and P12. New blood vessels (BVs) arising from ascending veins give rise to a dense capillary bed ( Coelho-Santos and Shih, 2020 ). BVs and macroglial cells interact during postnatal development. OPCs from ventral origin use blood vessels to migrate to the dorsal telencephalon during embryonic development ( Lepiemme et al., 2022 ). Whether this also stands for postnatal cortical OPCs remains unknown. The role of BVs in astrocyte progenitor’s migration is also unknown. A significant part of cortical Aldh1l1-positive cells, which include differentiated astrocytes as well as progenitors, are in contact with BVs at P1, but this population drops at P5, in the middle of the highly proliferative and migratory phase of astrocyte progenitors ( Clavreul et al., 2019 ; Freitas-Andrade et al., 2023 ). BVs have also been shown as a signalling source for both oligodendrocyte and astrocyte maturation ( Paredes et al., 2021 ). A better characterization of how macroglial cells organize and relate to BV during the postnatal period is however necessary. In this study, we developed VeCell , a new Fiji plugin to characterize macroglial cell density, distribution and distance to BVs in the developing and mature brain. Macroglial cells were immunolabelled for Sox 9 and 10 which regulate astrocyte and oligodendrocyte differentiation from glial precursors ( Stolt and Wegner, 2010 ). Sox9 is expressed in NPCs and controls their switch to glial progenitors ( Güven et al., 2020 ; Kang et al., 2012 ; Stolt, 2003 ). Its expression decreases during OligoLC specialisation, while increasing in astrocytes ( Nagao et al., 2016 ; Klum et al., 2018 ). Sox10 is expressed in all OligoLCs, which are, during postnatal development, a mix of mature oligodendrocytes and OPCs originating either from cortical postnatal or ventral embryonic progenitors. In these cells, Sox10 regulates the transcription of myelin-forming genes ( Foerster et al., 2024 ; Stolt et al., 2002 ). Focusing on the somatosensory cortex, we found that Sox9+ cell density is homogeneous across cortical layers and peaks at P5, while Sox10+ cell density is maintained during postnatal development and is always higher in the deeper layers of the cortex. Sox9+ cells are distributed regularly within the parenchyma from P5. In contrast, Sox10+ cells are randomly distributed with varying distance between cells. Volume and branching of the cortical vascular network increase between P5 and P15. Sox9+ and Sox10+ cells become closer to BVs from P15. Absence of MLC1, an astrocyte protein enriched in perivascular processes does not alter astrocyte density and distribution. Materials and Methods Animal experiments and ethical approval C57BL/6JRj, aged 5 to 60 days were purchased from Janvier labs (Le Genest-Saint-Isle, France) and kept in pathogen-free conditions. All experiments and techniques complied with (i) the European Directive 2010/63/EU on the protection of animals used for scientific purposes and (ii) the guidelines issued by the French National Animal Care and Use Committee (reference: 2013/118). Animal cohorts were made of males and females. Brain sections preparation Mice were sacrificed by cervical dislocation or decapitation depending on mouse age. Brains were carefully collected and fixed in PBS/PFA 4% overnight at 4°C. After dehydration in 30% sucrose, brains were cut into 80-µm-thick sections using a Leitz (1400) cryomicrotome and kept at −20°C in storage solution (PBS/glycerol 30%/ethylene glycol 30%). Immunofluorescent staining Brain sections were rinsed three times 15 min in PBS and incubated in a blocking solution (BS) for 1h at room temperature (RT). BS were PBS/normal goat serum 5%/Triton X-100 0.5% (Sox10) or PBS/Triton X-100 0.5% (Sox9). Sections were incubated with primary antibodies diluted in the same BS at 4°C overnight. After three rinses of 15 min in PBS, the sections were incubated for 2h at RT with secondary antibodies and Hoechst reagent, rinsed in PBS, and mounted in Fluoromount G (Southern Biotech, Birmingham, AL). Images were acquired using a wide-field Axio Zoom V16 microscope and its Apotome V2 module (Zeiss) or spinning disk W1 (Nikon). Primary and secondary antibodies Primary antibodies used were polyclonal goat IgG anti-Sox9 (AF3075 Biotechne/R&D, 1:500), monoclonal mouse/IgG2a anti-Sox10 (66786-1-Ig Proteintech, 1:500), AlexaF647-coupled Polyclonal Goat IgG anti-Pecam-1/Cd31 (AF3628R-MTO, Biotechno, 1:300). Secondary antibodies used were AlexaF555-coupled Goat anti-mouse IgG2a (A-21137 thermo Fisher, 1:1000) and AlexaF555-coupled Donkey anti-Goat IgG (A-21432 thermo Fisher, 1:1000). Nuclei were stained using Hoechst (Thermo fisher 62249, 1/2000). Image analysis Bins used to draw the ROI were defined based on cell density using nuclear staining Hoechst as presented in Figure 1A . Bin 1 corresponds to the upper part of the cortex, Bin 2 to the deeper part. Layer 1 of the cortex was excluded because of its different organization and morphology of astrocytes. Download figure Open in new tab Figure 1. Macroglial cells density during postnatal cortical development. A. Schematic representation of the studied area in the mouse brain (left) and developmental timeline (right) of the macroglial cells expressing Sox9 (red) or Sox10 (magenta). B. Representative Axio Zoom microscopy images of Hoechst (Blue), Sox9+ (red) or Sox10+ (magenta) showing the Region Of Interest (ROI) within the somato-sensory cortex (Bin 1: upper layers, Bin 2: lower layers). C,D. Spinning disk W1 Z-stack projection images of Sox9+ (red) (C) or Sox10+ (magenta) (D) cells detected by immunofluorescence at P5 and P60 in Bin 1 or Bin 2 of somatosensory cortical sections. E . Representative Axio Zoom microscopy Z-stack projection image of Sox9+ cells detected by immunofluorescence (top, white) on a P15 somatosensory cortical slice and the mask created by the VeCell Fiji plugin (down, red). F, G. Analysis of the Sox9+ (F) and Sox10+ (G) cells densities shown as a number of cells x10 2 per mm 3 . N= 3 mice, n=1 section per animal, results are shown as mean of the results on 4 ROI ± SD. Statistics: Mann Whitney test between ages (F) or between layers (G). The analysis was conducted in each region of interest using a custom-developed plugin called VeCell ( https://github.com/orion-cirb/VeCell ) for the Fiji software ( Schindelin et al., 2012 ). This plugin integrates the Bio-Formats ( Linkert et al., 2010 ), CLIJ ( Haase et al., 2020 ) and 3D ImageJ Suite ( Ollion et al., 2013 ) libraries. VeCell consists in three analysis steps. 1) Macroglial cell detection in the Sox9/Sox10 channel: detection is performed using the 2D-stitched version of the Cellpose algorithm ( Stringer et al., 2021 ). A custom model was trained through the human-in-the-loop workflow available in the Cellpose GUI. This model was initialized with the ‘cyto2’ pre-trained Cellpose model and fine-tuned using a dataset of 36 images collected across three developmental stages (P5, P15, and P60). Of these, 8 images were reserved for validation. The model was trained using the following hyperparameters: learning rate = 0.1, weight decay = 0.0001, and number of epochs = 500. Training and validation loss curves were monitored to ensure convergence. Model performance was evaluated on a separate test set comprising 6 images (2 per developmental stage). The fine-tuned model achieved a significant improvement in mean average precision (mAP) at an IoU threshold of 0.5, increasing from 0.674 (for the ‘cyto2’ pretrained model) to 0.836. In addition, a separate model was trained specifically for P1 developmental stage images to account for the more elongated cell morphology and a lower signal-to-noise ratio. A dataset of 25 images was used for training, with 5 images reserved for validation. The P1-specific model was initialized from the ‘cyto2’ pretrained weights and fine-tuned using similar hyperparameters: learning rate = 0.1, weight decay = 0.0001, and number of epochs = 1000. Model performance was evaluated on a test set of 6 images, demonstrating a substantial improvement in mAP at an IoU threshold of 0.5: from 0.539 for the ‘cyto2’ pretrained model to 0.778 after fine-tuning. These models were deemed suitable for processing the entire dataset. During inference, the following parameters were used: diameter = 20, flow threshold = 0.4, cell probability threshold = 0.0, and stitching threshold = 0.5. Detected 3D cells were filtered based on volume to reduce false positives. Only cells with a volume between 30 µm³ and 3000 µm³ were retained for downstream analysis. 2) Blood vessel detection in the Pecam-1 channel: Images were normalized using the Quantile-Based Normalization algorithm (“‘Quantile Based Normalization’ ImageJ PlugIn,” n.d.) ensuring intensity consistency across the dataset. A 3D median filter ( radius x = 4, radius y = 4, radius z = 1 ) was applied to reduce noise while preserving vessel borders. Subsequently, a slice-by-slice 2D Difference of Gaussians filter (σ 1 = 4, σ 2 = 8) was applied to enhance vessel structures. A binary mask was generated using the Triangle automated thresholding method. The process was repeated with a second 2D Difference of Gaussians filter (σ 1 = 7, σ 2 = 14) , producing a second binary mask. These two binary masks were then merged using a maximum point operation to ensure comprehensive vessel detection. Postprocessing includes applying a 3D closing filter (radius x = 8, radius y = 8, radius z = 1) to fill gaps in vessel structures, followed by a 3D median filter (radius x = 1, radius y = 1, radius z = 1) to smooth vessel borders. The resulting binary mask was labelled in 3D to identify individual vessel structures, which are filtered by volume to retain only vessels exceeding 600 µm³. Finally, the binary mask was skeletonized using CLIJ library, and branches shorter than 30 µm were excluded to ensure only meaningful segments were included in subsequent skeleton analysis. 3) Computation and saving of metrics for macroglial cells and blood vessels. For macroglial cells, were calculated: volume, distance to the nearest neighbor, mean and maximum distances to the 10 nearest neighbors, distance to the nearest vessel, and nearest vessel’s diameter. Additionally, spatial statistical analysis was performed to identify clustering or dispersion patterns within the macroglial cell population. This was achieved using the 2D/3D Spatial Analysis Fiji plugin from the 3D ImageJ Suite library, which computes the distance function G. This function represents the cumulative distribution of distances between a typical point in the spatial pattern and its nearest neighbor ( Andrey et al., 2010 ). The spatial distribution index (SDI) is derived from this analysis, serving as a metric to quantify the deviation of the observed spatial distribution from a completely random distribution. For BVs, skeleton analysis was conducted using the Analyze Skeleton Fiji plugin ( Arganda-Carreras et al., 2010 ). The following metrics are obtained: total volume, total length, mean branch length, number of branches and junctions, mean diameter and diameter standard deviation. Statistical analysis Data shown on graphs are expressed as means and SD. Graphs and statistical analyses were performed using Graphpad Prism 8.0.2 software. For every data, we first verified that they followed a normal distribution using Shapirow-Wilk test. If not, we used Kruskal-Wallis and Mann-Whitney tests. If it is, we used a 2-way ANOVA followed by Sidak’s multiple comparisons test to analyse differences between Bins or Tukey’s multiple comparisons test, to analyse differences between ages. For SDI distribution, comparison to normality was performed using Kolmogorov-Smirnov test. For percentages, a chi-square test was used. Alpha thresholds for hypothesis verification were set at 0.05. Statistical tests are indicated in the Figure legends and in the supplementary table 1. Use of Artificial Intelligence Generated Content ChatGPT (version 5) was used to perform the final editing of this manuscript, including grammar and punctuation corrections, as well as improvements in clarity. It was not used to generate any content or the original version of the text. Results Cortical density of macroglial cells during postnatal development To characterize the postnatal development of cortical macroglial cells in situ, we immunolabelled brain sections from postnatal day (P) 1, P5, P15 and P60 for the transcription factors Sox9 and Sox10, focusing on the somatosensory cortex ( Fig. 1A-D ). Given previous studies showing different population dynamics across cortical layers ( Orduz et al., 2019 ), we analysed the upper (Bin 1) and lower (Bin 2) parts of the cortex separately. Bins were determined by cell density revealed by nuclear labelling ( Fig. 1B ). Sox9+ and Sox10+ cell detection was automated using an adapted version of the CellPose deep learning-based algorithm and Fiji software ( Schindelin et al., 2012 ; Stringer et al., 2021 ). Training on a set of 28 images collected at three developmental stages (P5, P15 and P60) allowed the detection of positive cells with 84% accuracy. At P1, immunolabelling was fainter and the nuclear shape was less circular than at later stages, requiring a specific training on 25 images. In this case, we obtained 78% of accuracy ( Fig. 1E and Methods). In the cortex, Sox9+ cells are predominantly glial progenitors at P1 and P5 and astrocytes from P15 ( Fig. 1A ). No difference was observed in Sox9+ cell density between the upper and lower cortical parts at any stage ( Fig. 1B, C, F and Table S1). Density peaked at P5, with a significant increase between P1 and P5 followed by a significant decrease until P60. This decrease was particularly pronounced between P5 and P15 (Bin 1: 2.4-fold; P5: 1857±137; P15: 780±70.49; P60: 426.7±69.89 10 2 /mm 3 cells; Bin 2: 2.3-fold; P5: 1816±181; P15: 775±34.66; P60: 359.7±110.8 10 2 /mm 3 cells). Sox10+ labelling was too faint to be analysed at P1. After P5, a small reduction in Sox10+ cell density was also observed in Bin 1 and after P15 in Bin 2 ( Fig. 1G ). A higher cell density was found in the lower part of the cortex (Bin 2), at all studied stages ( Fig. 1B, D, G and Table S1). We then compared the two cell populations. Sox9+ cells were more than 2 times more abundant than Sox10+ cells in the upper part of the cortex at P5. In the lower part, Sox9+ cells were more abundant at P5, but Sox10+ density was higher at P15 and P60 (Table S1). Altogether, VeCell enables the detection of macroglial cells based on Sox9 and Sox10 nuclear immunolabelling. It reveals disparities in density between these two cell populations during cortical postnatal development. Distribution of macroglial cells in the cerebral cortex during postnatal development We next analysed the distribution of macroglial cells during postnatal development of the somato-sensory cortex. First, we measured the mean distance between each cell and its nearest neighbour. Consistent with the decreased cell density of Sox9+ cells after P5, the mean distance between neighbouring Sox9+ cells progressively increased after P5 (Bin 1; P5: 12.6±0.2; P15: 17.1±0.2; P60: 22.5±1.4 µm) (Bin 2; P5: 13.0±0.5; P15: 18.0±0.5; P60: 25.6±0.9 µm). At P60, Sox9+ cells were also slightly closer to each other in the upper compared to the lower part of the cortex ( Fig. 2A ). Measurement of the mean distance between each Sox9+ cell and its 10 nearest Sox9+ cells gave similar results ( Fig. 2B , Table S1). For Sox10+ cells, no difference in the mean distance between adjacent cells nor with 10 nearest cells was observed between P5 and P60 ( Fig. 2C, D and Table S1). Consistent with density differences ( Fig. 1 ), Sox10+ cells were closer to each other in the lower part of the cortex from P5 ( Fig. 2C, D and Table S1). Download figure Open in new tab Figure 2. Macroglial cells distribution during postnatal cortical development A, B. Analysis of mean distance to the closest (A) or 10 closest (B) Sox9+ cells, shown as the mean distance for each cell of the studied area in the somato-sensory cortex. C, D. Analysis of mean distance to the closest (A) or 10 closest (B) Sox10+ cells, shown as the mean distance for each cell of the studied area in the somato-sensory cortex. E . Schematic summary of the G-function analysis of cell distribution. Cells can follow three main behaviours: grouped (SDI=0), randomly distributed or regularly spaced (SDI=1). The G-function is the cumulative distribution function (CDF) of the distance between a typical point (the labelled cell) in the pattern and it nearest-neighbour. The experimental CDF (black curve) is obtained for each ROI analysed and is compared to the CDF of randomly generated point patterns in the same area and with the same number of points (red curve). For each ROI analysed, comparison between the experimental and random CDF are computed as the Spatial Distribution Index (SDI). Distribution of SDIs in each experimental condition indicates the distribution pattern followed by the cells. F . Result of G-function analysis for Bin 2 Sox9+ (pink) and G. Sox10+ (purple) cells across postnatal development. N= 3 mice, n=1 section per animal, results are shown as mean of the results on 4 ROI (A-D) per section or the result of one ROI (F) ± SD. Statistics: Mann Whitney test between ages (A,B) or layers (C,D).. F, G Kolmogorov-Smirnov test. The spatial arrangement of cells within a population can vary from clustered to evenly spaced patterns, which may reflect distinct developmental processes such as cellular attraction or repulsion. We used the Fiji plugin “ 2D/3D Spatial Analysis” to assess the deviation from spatial randomness ( Andrey et al., 2010 ). We computed the G-function, the cumulative distribution function (CDF) of the distance between a labelled cell and its nearest-neighbour. Shorter distances suggest clustering, while longer distances indicate regular spacing ( Fig. 2E ). The experimental CDF was compared to the CDF of randomly generated patterns within the same area and with the same number of cells. For each region of interest (ROI), this comparison was computed as the Spatial Distribution Index (SDI) ( Andrey et al., 2010 ). Distribution of SDIs indicates the cell distribution pattern: uniformly distributed SDIs from 0 to 1 reflects a random distribution of cells. SDIs near 0 suggest clustering. SDIs near 1 indicate regular spacing ( Andrey et al., 2010 ) ( Fig. 2E ). Given the higher density of Sox10+ cells in the lower part of the cortex, we first focused our study on this region. For Sox9+ cells, the SDIs distribution was significantly different from the random distribution from P5 ( Fig. 2F , Table S1), and values were not different from 1 (Bin 2: P5: 0.98±0.05; P15: 0.98±0.04; P60: 0.88±0.3), revealing a regular distribution. At P1, however, SDIs were not different from normal distribution, indicating a more random cell distribution. Same results were found in the upper layers of the cortex (Table S1). These results indicated that, from P5 onwards, Sox9+ cells are regularly organized. For Sox10+ cells, the SDIs distribution differed between stages. At P5 and P15, distribution was significantly different from random but not at P60 ( Fig. 2G ; Table S1). However, SDIs were always significantly different from 1. The same result was found in the upper part of the cortex (Table S1). This indicates that organisation of Sox10+ population always differs from a regular distribution and the mature random distribution is established by P60. Overall, our findings demonstrate that Sox9+ and Sox10+ cells follow distinct postnatal trajectories adopting specific distribution patterns in the somatosensory cortex. Sox9+ cells maintain a regular distribution from P5; Sox10+ cells are denser in deeper cortical layers, are never regularly organized and change their spatial distribution during postnatal development. Distribution of glial cells relative to the vascular compartment Macroglial cells are closely associated with the brain vasculature. In the adult CNS, astrocytes entirely cover the BVs through specialized processes called endfeet or perivascular astrocyte processes (PvAP) ( Cohen-Salmon et al., 2020 ). The formation of PvAPs, which occurs from birth to P15, plays a crucial role in BV maturation ( Cohen-Salmon et al., 2025 ; Freitas-Andrade et al., 2023 ; Gilbert et al., 2019 , 2021 ; Mondo et al., 2020 ). Furthermore, parts of OPCs migrate along BVs during embryonic development ( Lepiemme et al., 2022 ). To better understand the relationship between macroglial cells and BVs during postnatal development we implemented VeCell with BV detection, immunolabeling endothelial cells for Pecam-1 ( Fig. 3A, B ). The cortical vascular volume was normalized on the cortical volume and analysed at P1, P5, P15 and P60. It changed during postnatal development: while stable between P1 and P5, a global increase in vascular volume was observed between P5 and P15 ( Fig. 3C ) due to postnatal angiogenesis and capillary bed extension ( Coelho-Santos and Shih, 2020 ) (Bin 1; P5: 0.08±0.009; P15: 0.1±0.01 µm 3 /µm 3 ) (Bin 2; P5: 0.08±0.007; P15: 0.08±0.01 µm 3 /µm 3 ). After P15, the vascular volume decreased, probably related to the cortical growth ( Fig. 3A, D and Table S1). Vessel branching followed the same trends ( Fig. 3F and Table S1). Of note, the vascular volume and branching were globally slightly reduced in the lower part of the cortex compared to the upper part but this difference was only significant at P15 for the branching ( Fig. 3D, E, F and Table S1). Download figure Open in new tab Figure 3. Vascular system organisation during postnatal cortical development A. Representative Axio Zoom microscopy image of P1, P5, P15 and P60 somatosensory cortical section stained for blood vessels (white) detected by immunofluorescence on Pecam-1. B . Representative Axio Zoom microscopy Z stack projection image of P15 somatosensory cortical section stained for blood vessels (white) detected by immunofluorescence on Pecam-1 with the superimposed masks created by the VeCell Fiji plugin (in blue). i and ii are detailed of boxed area. i, BVs mask created by the VeCell Fiji plugin (blue); ii, skeleton mask of the BV for branching analysis (green). C . Representative Axio Zoom microscopy Z-stack projection images of P5 and P15 somatosensory cortical sections in Bin1, stained for blood vessels detected by immunofluorescence for Pecam-1 (white). D. Analysis of the vascular volume reported as BVs volume normalized by cortical volume. E . Representative Axio Zoom microscopy Z-stack projection images of P15 somatosensory cortical sections Bin1 (left) or Bin2 (right), stained for blood vessels detected by immunofluorescence for Pecam-1 (white). F . Analysis of the vascular branching as the number of 10 3 branches per mm 3 of BVs. N= 3 mice, n=2 sections per animal. Results are shown as mean of the results on 4 ROI per section ± SD. Statistics: 2-way ANOVA; p(age): result of ANOVA for age effect; p(Bin): result of ANOVA for cortical layer effect. We analysed the distance between macroglial cells and their nearest BV in the somatosensory cortex at all stages ( Fig. 4 .A). We calculated both the mean distance to BVs for all cells ( Fig. 4C and E ) and the percentage of cells with the nucleus directly apposed to BVs ( Fig. 4D, F ). The distance to BV was the highest at P5, and similar for Sox9+ and Sox10+ cells (Fig. C-E, Table S1). In contrast, from P15, Sox9+ cells were closer to BVs than Sox10+ cells and a higher proportion of Sox9+ were found apposed to BV ( Fig. 4B-D , Table S1). Download figure Open in new tab Figure 4. Macroglial cells distribution in relation to blood vessels during postnatal cortical development A. Representative Axio Zoom microscopy Z-stack projection image of a P15 somatosensory cortical section stained for blood vessels (white) detected by immunofluorescence on Pecam-1 with the superimposed masks created by the VeCell Fiji plugin. BVs mask is in blue and Sox9+ cells are color coded for their distance to nearest BV from magenta (close to BV) to yellow (far from BV). i and ii are detailed of boxed area. i, immunofluorescence of Pecam-1-labeled BVs (white) and Sox9+ cells (red); ii, BVs mask created by the VeCell Fiji plugin (blue) superimposed to the Pecam-1 staining (white) and mask of Sox9+ cells color-coded for their distance to nearest BV. B. Representative Axio Zoom microscopy Z-stack images of Sox9+ (red) or Sox10+ (magenta) cells and Pecam-1 (white) detected by immunofluorescence at P1, P5, P15 and P60 in Bin 2 of somatosensory cortical sections. C, E. Analysis of the mean distance to nearest BV of Sox9+ (C) and Sox10+ (E) cells. D, F. Analysis of the Sox9+ (D) and Sox10+ (F) percentage of nuclei apposed to BV (distance to nearest BV=0 µm). N= 3 mice, n=1 section per animal. Results are shown as mean of the results on 4 ROI per section ± SD. Statistics: Mann Whitney test between ages (C, E) and chi-square test between ages (D, F) Download figure Open in new tab Figure 5. Sox9+ cell density and distribution in the Mlc1 KO model. A. Spinning disk W1 Z-stack projection images of P60 upper and lower parts of the somatosensory cortex after immunostaining for Sox9 (red) and BV (grey). B . Masks created by the VeCell Fiji plugin on P60 Mlc1 KO Axio Zoom microscopy Z-stack projection image, BVs mask is in blue and Sox9+ cells are color coded for their distance to nearest BV from magenta (close to BV) to yellow (far from BV). C. Analysis of the vascular volume reported as BVs volume normalized by cortical volume. D. Analysis of the vascular branching as the number of 10 3 branches per mm 3 of BVs. E . Analysis of the Sox9+ cells densities shown as a number of cells x10 2 per mm 3 . F, G . Analysis of mean distance to the closest (F) or 10 closest (G) Sox9+ cells, shown as the mean distance for each cell of the studied area in the somato-sensory cortex. H. Result of G-function analysis for Sox9+ cells in Bin 1 and 2 in the two genotypes. N= 3 WT and 4 Mlc1 KO mice, n=3 section per animal, results are shown as mean of the results on 4 ROI per section ± SD. Statistics: C,D,E,F,G,I: 2-way ANOVA; p (genotype) : result of ANOVA for genotype effect; p (Bin) : result of ANOVA for cortical layer effect. H: Kolmogorov-Smirnov test Altogether, our approach allows to measure the distance between macroglial cells and BVs. We shows that macroglial cells are more distant to BVs at P5. From P15, Sox9+ cells are closer to BVs than Sox10+ cells, suggesting distinct interactions between these macroglial populations and the vasculature. Thus, we reveal different developmental changes in the spatial relationship between macroglial cells and BVs. Macroglial cell density and distribution are not altered in Mlc1 KO cortex MLC1 is a membrane protein expressed by the astrocyte lineage in the brain whose absence leads to Megalencephalic Leukoencephalopathy with subcortical Cysts (MLC), a rare type of leukodystrophy ( Passchier et al., 2024 ). MLC1 expression starts at P5. The protein progressively localizes within astrocyte perivascular processes (PvAP) progressively and forms a junctional complex between PvAPs with GlialCAM, which is mature around P15 ( Gilbert et al., 2019 ; Teijido et al., 2007 ). In Mlc1 KO mice, the gliovascular interface is altered during development: astrocyte polarity and vascular coverage do not develop properly and vascular functions are altered, including arteriolar contractility, CSF drainage and neurovascular coupling ( Gilbert et al., 2021 ). Thus, we hypothesized that absence of MLC1 could also perturb astrocyte spatial arrangement. On P60 somatosensory cortical sections, we observed no difference in the vascular volume and branching ( Fig. 4A-D ). We found no difference in Sox9+ cell density ( Fig. 4A and E ). We observed however a slight decrease in the distance between cells in Mlc1 KO (Bin 1; Mlc1 WT: 23.4±1.7; Mlc1 KO: 20.6±1.8 µm; Bin 2; Mlc1 WT: 27.6±1.2; Mlc1 KO: 23.7±1.6 µm). G-function-assessed distribution pattern was similar between genotypes ( Fig. 4H ) and similar to our previous observation for Sox9+ cells: SDI distribution different from the normal distribution and not different from 1 ( Fig. 4G and Fig. 2 ). Finally, the mean distance of Sox9+ cells to the closest BV was similar in Mlc1 KO and controls ( Fig. 4H ). We show that the vascular network as well as the distribution and density of Sox9+ cells, remain unaffected in the absence of MLC1. Discussion Here we developed VeCell , a new Fiji plugin which allows the characterization of cell distribution. We focused our study on macroglial cells during postnatal development, based on the labelling of two transcription factors, Sox9 and Sox10, and of the vascular system. Our approach first confirmed previously reported results regarding macroglia and vascular development. The density of Sox9+ cells was highest at P5 compared to P1 and P15/P60, similar to observations done with immunolabeling for Aldh1l1, an astrocyte-specific marker ( Freitas-Andrade et al., 2023 ). We showed a higher density of Sox9+ cells compared to Sox10+ at P5 but not at P60, consistent with the reported intense proliferation of astrocyte progenitors around P4 ( Clavreul et al., 2019 ) and the reduced expression of Sox9 in progenitors differentiating into OligoLC ( Klum et al., 2018 ). We observed a smaller Sox10+ population in the upper part of the somatosensory cortex at all developmental stages, as previously described for OPCs ( Ogino et al., 2020 ). This effect has been shown to rely on Reelin secreted by Cajal-retzius cells during late embryonic development, which acts as a driving force for OPC migration ( Barber et al., 2022 ; Ogino et al., 2020 ). It is also consistent with the known higher density of myelinating oligodendrocytes in the lower cortical layers ( Orduz et al., 2019 ). We observed a random distribution of Sox10+ cells within cortical layers as previously showed ( Orduz et al., 2019 ). In contrast, we showed that Sox9+ density was identical between layers of the cortex and displayed a regular distribution indicating similar distances between cells. This is in line with the well-established organisation of astrocytes which occupy exclusive, non-overlapping domains that completely tile the cortical parenchyma in the mature brain, with each cell occupying a comparable volum ( Bushong et al., 2004 , 2002 ; Halassa et al., 2007 ). Using Vecell we could also reproduce previous results demonstrating the existence of a second wave of angiogenesis between P5 to and P15 ( Coelho-Santos and Shih, 2020 ; Freitas-Andrade et al., 2023 ). To summarize, VeCell provides a fast and reliable tool to study macroglial distribution, vascular postnatal development, and their interrelationship. This approach can assist the gliovascular research community in accurately quantifying these parameters across different models and brain regions. A key aspect of our study was the description of distinct patterns of distribution for macroglial cells. We found Sox9+ astrocytes to be regularly distributed within the parenchyma as early as P5, suggesting an extremely precise and coordinated colonization of the entire cortical parenchyma by macroglial progenitors/astrocytes. In contrast, Sox10+ cells were more randomly distributed. Astrocyte tiling of the parenchyma is crucial to neuronal function ( Baldwin et al., 2021 ). Such very early acquired organisation suggests the presence of active mechanisms of active attraction and repulsion mechanisms between astrocyte progenitors which remain to be fully understood. In contrast, Sox10+ cells displayed a random distribution, suggesting different mechanisms orchestrating oligodendrocytes and OPCs postnatal colonization of the cortical parenchyma. The interaction between macroglial cells and BVs has gathered increasing interest in both developmental and disease contexts ( Lepiemme et al., 2022 ; Niu et al., 2019 ; Watkins et al., 2014 ). We extended our plugin’s functionality to calculate the distance between macroglial cells and BVs. Both Sox9+ and Sox10+ cells were found to be closer to BVs at P15/P60 compared to P5. This result might simply reflect the increased BVs density in the parenchyma. However, we cannot rule out the possibility of active migration mechanism after P5, potentially driven by signals from vascular cells. At P1, Sox9+ cells were closer to BV compared to P5, despite a similar vascular network and an increased Sox9+ cell density. This could, in contrast, indicate active mechanisms to repel Sox9+ cells from BVs or a transient phase of cell migration along the vessels in the first days after birth as previously suggested ( Freitas-Andrade et al., 2023 ). Interestingly, Sox9+ nuclei were closer to BVs than Sox10+ which is consistent with the known higher interaction between astrocytes and BVs, compared to oligodendrocytes, including the complete coverage of BVs with PvAPs by P15 ( Gilbert et al., 2021 ; Mathiisen et al., 2010 ). To further assess the utility of VeCell , we analyzed a mouse model deficient for the astrocyte protein MLC1, in which astrocyte polarity and PvAP formation are disrupted ( Gilbert et al., 2021 ). We found no difference in vascular organisation, consistent with our previous report ( Gilbert et al., 2021 ). The regular astrocyte spatial distribution was preserved. Thus, altered perivascular coverage does not necessarily imply a change in astrocyte distribution. We cannot however exclude the possibility of transient developmental changes as previously reported in this model ( Gilbert et al., 2021 ). Beyond the study of MLC, this analysis could be applied to identify defects in macroglial production and/or distribution in various cerebrovascular and neurodevelopmental disorders that emerge during late brain development, such as cavernomas, autoimmune diseases including multiple sclerosis and Neuromyelitis Optica, arteriovenous malformations, muscular dystrophies affecting the brain, and other leukodystrophies ( Cohen-Salmon et al., 2025 ). While the destruction or abnormal proliferation of astrocytes, oligodendrocytes, and OPCs have been frequently documented in diseases of the mature brain, impairments in their production and spatial organization during postnatal development—whether or not related to the vascular system—remain largely unexplored Altogether, we present a reliable, freely available Fiji plugin for analyzing macroglia organization using simple brain sections and low-resolution microscopy. This tool successfully reproduces previously reported findings, highlighting the robustness of our approach. Moreover, it revealed strong differences between the developmental trajectories of astrocyte and oligodendrocyte lineages. Notably, we observed that astrocytes adopt a remarkably regular distribution pattern that is established very early during postnatal development. Acknowledgments We gratefully acknowledge the Orion technological core (IMACHEM-IBiSA) of Center for Interdisciplinary Research in Biology for their support, and especially Estelle Anceaume and Julien Dumont for assisting us with the Axio Zoom microscopy image acquisition. We thank the whole “physiology and physiopathology of the gliovascular unit” team for helpful comments and contribution on the VeCell plugin functionality. Funder Information Declared UniversitM-CM-) Paris Sciences et Lettres, https://ror.org/013cjyk83 Agence Nationale de la Recherche , ANR-23-CE16-0030 Fondation pour la Recherche MM-CM-)dicale , EQU202303016292 Footnotes Funding statement: This work was funded by Université PSL (Junior Fellow to ACB), ANR (ANR-23-CE16-0030 to ACB) and the Fondation pour la Recherche Médicale (EQU202303016292 to MCS). The “Physiology and Physiopathology of the Gliovascular Unit” research group at the Collège de France’s CIRB is affiliated with PSL-NEURO and funded by Paris Sciences et Lettres (PSL) University. Addition of postnatal day 1 data; addition of the study in a Mlc1 KO mouse model; References ↵ Andrey , P. , Kiêu , K. , Kress , C. , Lehmann , G. , Tirichine , L. , Liu , Z. , Biot , E. , Adenot , P.-G. , Hue-Beauvais , C. , Houba-Hérin , N. , Duranthon , V. , Devinoy , E. , Beaujean , N. , Gaudin , V. , Maurin , Y. , Debey , P ., 2010 . 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Share Spatio-temporal dynamics of macroglial cell organisation and proximity to blood vessels during postnatal development Naomie Guille , Héloïse Monnet , Tristan Hourcade , Philippe Mailly , Martine Cohen-Salmon , Anne-Cécile Boulay bioRxiv 2025.01.14.632893; doi: https://doi.org/10.1101/2025.01.14.632893 Share This Article: Copy Citation Tools Spatio-temporal dynamics of macroglial cell organisation and proximity to blood vessels during postnatal development Naomie Guille , Héloïse Monnet , Tristan Hourcade , Philippe Mailly , Martine Cohen-Salmon , Anne-Cécile Boulay bioRxiv 2025.01.14.632893; doi: https://doi.org/10.1101/2025.01.14.632893 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17690) Bioengineering (13892) Bioinformatics (41936) Biophysics (21451) Cancer Biology (18588) Cell Biology (25499) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88603) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15152) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)