1. Neuroscience

Developmental synchrony of retinal waves, apoptosis, and angiogenesis in postnatal retina

  1. Michael A Savage  Is a corresponding author
  2. Cori Bertram
  3. Jean de Montigny
  4. Courtney A Thorne
  5. Rachel Queen
  6. Majlinda Lako
  7. Gerrit Hilgen  Is a corresponding author
  8. Evelyne Sernagor
  1. Biosciences Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
  2. School of Geography and Natural Sciences, Northumbria University, United Kingdom
https://doi.org/10.7554/eLife.111419.3
Share this article
Cite this article
  1. Michael A Savage
  2. Cori Bertram
  3. Jean de Montigny
  4. Courtney A Thorne
  5. Rachel Queen
  6. Majlinda Lako
  7. Gerrit Hilgen
  8. Evelyne Sernagor
(2026)
Developmental synchrony of retinal waves, apoptosis, and angiogenesis in postnatal retina
eLife 15:RP111419.
https://doi.org/10.7554/eLife.111419.3
  2. Download BibTeX
  3. Download .RIS

eLife Assessment

This study identifies apoptotic retinal ganglion cells as a potential source of ATP-mediated activation of PANX1 channels that initiates developmental retinal Ca²⁺ waves and coordinates microglial activation and vascular outgrowth during postnatal maturation. The work is important because it proposes an integrative framework linking programmed cell death, spontaneous neural activity, immune responses, and angiogenesis into a self-regulating developmental loop. Although the mechanistic conclusions would benefit from complementary genetic validation, the study provides a convincing foundation for future investigations into the coordination of neural circuit development and tissue remodeling.

https://doi.org/10.7554/eLife.111419.3.sa0

Abstract

Postnatal mouse retinal development is a multi-faceted process involving the coordinated interaction of spontaneous neural activity as retinal waves, vascular plexus growth, and programmed cell death. While these processes are known to interact at a coarse scale, the specific mechanisms integrating them have remained elusive. Using large-scale, wide-field calcium imaging, high-density multielectrode array recordings, single-cell RNA sequencing, and immunohistochemistry, we characterise a tightly aligned centrifugal expansion pattern during retinal development. This pattern is common to stage II retinal wave onsets, vascular development, Heme oxygenase-1 (Hmox1) expressing microglia, apoptotic cell markers, and a novel set of auto-fluorescent cluster complexes (ACCs) identified in this study. Apoptotic cells are known to upregulate functional pannexin-1 (PANX-1) hemichannels. These voltage-gated channels release purinergic molecules which act as ‘eat me’ signals to neighbouring microglia. PANX-1 hemichannel blockade with the drug probenecid results in a profound decrease in spontaneous wave frequency and strength, suggesting that retinal waves are indeed triggered by these apoptotic cells. Taken together, our observations suggest that spontaneous waves are initially triggered in hotspots by hyperactive apoptotic retinal ganglion cells (RGCs) in unvascularised retinal areas. These apoptotic cells release purinergic molecules via PANX-1 hemichannels, leading to wave generation. This hyperactivity leads to local hypoxic conditions, which, coupled with high extracellular ATP concentrations, promotes angiogenesis. Once blood vessels reach a particular hotspot, ATP release activates Hmox1-positive microglia, which engulf the dying RGCs, creating the auto-fluorescent clusters. Herein, we present a unified mechanism linking causally linking early neural activity, programmed cell death, and angiogenesis in the mammalian retina.

Introduction

Spontaneous neural activity is a hallmark of developing neural tissue (O’Donovan, 1999). This is ubiquitous across species and present in both the peripheral and central nervous systems (CNSs). In the retina, this takes the form of waves of electrical activity that sweep across the retinal ganglion cell (RGC) layer. It occurs during very precise and restricted temporal windows, overlapping with other important developmental events such programmed cell death, neurite outgrowth, synapse formation, and angiogenesis. This research focuses on the first postnatal week at which time stage II retinal waves are present. They are mediated by cholinergic synaptic transmission through starburst amacrine cells (SACs) and last until postnatal day (P)10 (Bansal et al., 2000; Feller et al., 1996; Sernagor and Grzywacz, 1996; Sernagor and Grzywacz, 1999; Wong et al., 1998; Zhou and Zhao, 2000). While the role of spontaneous activity in refining network connectivity is well documented, the endogenous factors that initiate and spatially constrain these waves, particularly their link to non-neuronal maturation, remain largely unexplored.

During the first postnatal week, the mouse retina undergoes angiogenesis, with the superficial vascular plexus (SVP) extending from the optic nerve head (ONH) at P0 towards the peripheral retina by P7 (Paredes et al., 2018). Local hypoxia and gradients in metabolic demand drive the outward expansion of the vascular plexus (Chan-Ling et al., 1995; Gariano and Gardner, 2005). Stage II retinal waves act as this metabolic driver with activity increasing in size and frequency from P2 before declining sharply at P7 (Maccione et al., 2014; Weiner et al., 2019).

Microglia have been shown to have an intimate connection to the progression of vascular development in the retina. They localise to the endothelial tip cells and interact with the developing vasculature endothelium (Liu et al., 2026). A specific subgroup of microglia expressing the Homx1 gene has been shown to display an annular pattern of distribution around the growing vascular plexus edge (Martineau et al., 2026). Depleting microglia leads to a significant decrease in expansion and density of retinal blood vessels, and reintroduction of microglia results in reversal and recovery of the SVP (Checchin et al., 2006).

Developmental apoptosis in the murine retina follows a centrifugal wave pattern mirroring both vascular growth and the microglial annulus. These apoptotic waves peak during the first postnatal week, after which they decline by P7 (Anderson et al., 2019; Farah and Easter, 2005). This process is functionally linked to remodelling via signalling molecules such as ATP, which is released through PANX-1 hemichannels and acts as a dual-purpose driver. It triggers angiogenesis (Umaru et al., 2015; Zhou et al., 2022) while simultaneously serving as an ‘eat me’ signal that transforms quiescent, ramified microglia into the amoeboid, phagocytic state required to engulf dying cells (Chekeni et al., 2010).

While retinal wave progression, developmental apoptosis, and angiogenesis occur concurrently in the postnatal mouse retina, existing literature lacks a cohesive framework that accounts for their interdependence. Despite their overlapping timelines, these processes are frequently studied in isolation, leaving the functional links between neural activity, cell death, and vascularisation largely unexplored.

In this study, we identify a novel population of auto-fluorescent cluster complexes (ACCs) that form an annulus beneath the leading edge of the SVP during the P0–7 developmental window. We characterise the genetic makeup of these ACCs with single-cell RNA sequencing (scRNA-seq), which proves they are complexes of microglia and dying RGCs. This interaction is further bolstered by immunohistochemical profiling, which shows that microglia are attracted to the ACCs’ local area and that a specific Homx1-expressing subtype contains dying RGC fragments in their intracellular vesicles. We then show that purinergic signalling through highly expressed apoptotically associated PANX-1 hemichannels is both necessary for proper microglial activation, ACC formation, and stage II retinal wave generation. Together, these results provide a unified framework for the timely vascularisation of the postnatal retina.

Results

Centrifugal expansion pattern is shared between ACCs and vascular development in postnatal mouse retina

The hypothesis that retinal waves, apoptosis, and angiogenesis are synchronised in the developing retina emerged from the discovery of sparse ACCs, which form a distinct, tight annulus around the ONH at P2–3 (Figure 1A). These ACCs appear sequentially in eccentricity outward from P2, disappearing at the retinal periphery by P7. This distribution precisely mirrors the expansion of the SVP, which extends from the ONH towards the periphery between P7 and P9 (Figure 1A, B, and G). The ACCs exhibit broad-spectrum fluorescence across multiple wavelengths (Figure 1C) and are situated in close proximity to, but do not overlap with, SACs (Figure 1D). These cells are localised exclusively within the ganglion cell layer (GCL) and are entirely absent from the inner nuclear layer (INL; Figure 1F). Notably, the ACCs consistently trail the leading edge of the vascular front across all developmental stages, with peak excentric positional change observed between P2 and P3, suggesting a mechanistic role in SVP guidance (Figure 1G). Quantitative analysis further reveals significantly elevated blood vessel branching in ACC-associated regions compared to equivalent eccentricities where ACCs are absent (Figure 1E). To characterise the molecular identity of these cells and their potential role in synchronising retinal waves with angiogenesis, we performed scRNA-seq.

Figure 1 with 1 supplement see all
Download asset Open asset
Auto-fluorescent cluster complexes (ACCs) display centrifugal development pattern during the first postnatal week in C57/BL6 and align with vasculature development.

(A) Mouse retinal wholemounts displaying ACCs (green) across postnatal day (P)3–6. (B) Mouse retinal wholemounts (P3–6) displaying superficial vascular plexus (SVP) (labelled with isolectin B4 [IB4]). Scale bar is equal to 1 mm. (C) Zoom into one auto-fluorescent cluster complex (P5) visualised at three different wavelengths without any additional immunolabeling, indicating intrinsic auto-fluorescent signals. (D) Retinal sections showing VAChT (red) expression in a cluster area (upper panel, P4 retina) and in an area devoid of clusters (lower panel, P5 retina). (E) Scatter plot of blood vessel branch intersections (Sholl analysis). There are significantly more branches in ACC-positive areas (ACC+, green) than in ACC-negative areas (ACC–, purple). Median with interquartile range. (F) P5 cluster viewed at the ganglion cell layer (GCL) level and at the inner nuclear layer (INL) level in a whole mount. Green: ChAT; magenta: RBPMS. The cluster cells are visible only at the GCL level and are much larger than starburst amacrine cells (SACs) or retinal ganglion cells (RGCs) (in red). At the INL level, there are only SACs and no ACCs. Right side of panel is magnified version of box on left. (G) Box plot showing developmental changes in ACCs D1/D2 ratios. Each box illustrates the median (horizontal line) and interquartile range, with minimum and maximum values (whiskers). Asterisks indicate significant changes between consecutive days (one-way ANOVA with post hoc Tukey test). The red dotted line illustrates the percentage difference in values between consecutive days, showing peak difference between P3 and P4 and no further changes from P6 onwards. ****: p<0.0001, Mann-Whitney two-tailed test.

Figure 1—source data 1

Number of blood vessel intersections in ACC+ and ACC- regions, ACC D1/2 values for P2-P9.

https://cdn.elifesciences.org/articles/111419/elife-111419-fig1-data1-v1.xlsx

scRNA-seq reveals auto-fluorescent cell complexes as microglia and dying RGC aggregates in the postnatal mouse retina

ACCs from P5 dissociated retinas were enriched using fluorescence-activated cell sorting (FACS) and subjected to scRNA-seq, which revealed the presence of five distinct clusters (Figure 2A). P5 was chosen as pilot data suggested that timepoint had the largest fraction of ACCs. Following FACS, the cell suspension was centrifuged to meet the input requirements of the 10x Genomics microfluidic platform, resulting in an estimated ~50% reduction in yield. Haemocytometer quantification indicated that 9405 viable single cells were loaded onto the 10x chip. Given an expected capture efficiency of ~50%, approximately 5000 FACS-purified cells were recovered for scRNA-seq library preparation. Libraries were generated and sequenced to a depth of 50,000 reads per cell using Illumina technology.

Figure 2
Download asset Open asset
Genetic analysis of auto-fluorescent cluster complexes (ACCs) suggests that they are complexes of microglia and dying retinal ganglion cells (RGCs).

(A) Uniform Manifold Approximation and Projection (UMAP) plot of single-cell RNA sequencing (scRNA-seq) data derived from ACCs taken from postnatal day (P)5 retinas. Each cluster was identified based on the expression of retinal-specific cell markers. Highly expressed markers for each cluster are shown in subsequent panels. (B) Expression of microglia cell markers SPARC and AIF1. (C) Expression of endothelial cell markers DAB2 and F13A1. (D) Expression of proliferating cell markers STMN1 and TUBB3. (E) Expression of retinal progenitor cell (RPC) markers TOPA2 and CDK1. (F) Expression of neurogenic progenitor cell markers NEUROD1 and RORB. (G) Expression of retinal ganglion cell markers RBPMS and BAX.

Unsupervised clustering and Uniform Manifold Approximation and Projection (UMAP) visualisation identified five distinct cell populations (Figure 2A). Cluster 0 was identified as microglia, exhibiting high expression of canonical markers, including Aif1, Sparc, C1qb, Sall1, P2ry12, Cd68, Dock10, and Cx3cr1 (Figure 2B). Cluster 1 comprised endothelial cells, as evidenced by the expression of F13a1 and Dab2 (Figure 2C). Clusters 2 and 3 represented cycling populations: Cluster 2 was characterised by microtubule dynamics genes (Stmn1, Tubb3; Figure 2D), while Cluster 3 consisted of retinal progenitor cells expressing proliferation markers such as Top2a and Cdk1 (Figure 2E). Cluster 4 was identified as neurogenic progenitors based on the expression of Rorb and Neurod1 (Figure 2F).

Notably, the whole dataset exhibited high levels of the apoptotic marker Bax and the RGC-specific marker Rbpms (G), suggesting a transcriptomic signature for ACCs consistent with RGC apoptosis and engulfment by microglia. A comprehensive list of cluster-specific markers is provided in Supplementary file 1.

Microglia are attracted to ACCs in the postnatal mouse retina

Given that scRNA-seq analysis identified a prominent microglial component within ACCs, we next examined whether microglial distribution in situ was spatially coupled to the presence of ACCs. Immunolabelling for the microglial marker Iba-1 demonstrated strong co-localisation between microglial cells and ACCs (Figure 3A). Quantitative analysis confirmed that microglial density was significantly elevated in ACC-associated regions compared with matched retinal areas lacking clusters (p=0.0138). Across retinas, microglial density significantly increased when associated with ACCs (Figure 3B, p=0.0318), and there was a strong positive correlation observed between microglial cell density and ACC cell density (Figure 3C, p<0.0001).

Figure 3
Download asset Open asset
Microglia associate with the auto-fluorescent cluster complexes (ACCs).

(A) Representative micrograph of ACCs (white) surrounded by microglial cells labelled with Iba-1 (green). Scale bar = 100 µm. (B) Box plot (median and interquartile ranges) comparing microglia density within cluster areas (C+) versus cluster negative areas (C–). p<0.0138. Mann-Whitney two-tailed test. (C) Strong positive correlation between the densities of microglial cells and auto-fluorescent cluster cells (p<0.0001). (D) Method for measuring microglia density at different retinal eccentricities, with measures taken near the optic nerve head (ONH), between the ONH and clusters (behind) at clusters and ahead of clusters, further in the periphery, in the unvascularised part of the retina. The micrograph shows blood vessels labelled with isolectin B4 (IB4) (magenta), microglia labelled with Iba-1 (orange), and ACCs (white). Scale bar = 500 µm. (E) Box plot (with medians and interquartile ranges) showing that the density of microglia is significantly higher within clusters (p<0.01, Mann-Whitney, two-tailed test).

Figure 3—source data 1

Microglia cell densities across different regions of eccentricity in ACC+ and ACC- regions.

https://cdn.elifesciences.org/articles/111419/elife-111419-fig3-data1-v1.xlsx

Because microglial density varies as a function of retinal eccentricity during postnatal development, we assessed whether enrichment at ACC sites could be explained by underlying radial gradients. Regions of interest (ROIs) were sampled at four progressively increasing eccentricities from the ONH, with paired regions defined as ACC-positive (ACC+) or ACC-negative (ACC−) (Figure 3D). Consistent with developmental migration patterns, microglial density increased closer to the ONH in all samples (Figure 3E). However, when eccentricity was controlled, microglial enrichment was restricted to regions directly co-localised with ACCs. Only regions positioned at the same radial distance as the ACC annulus exhibited significantly elevated microglial density relative to matched ACC− regions (Figure 3E, ‘@ Cluster’, p<0.01). Areas located either peripheral (‘ahead’) or central (‘behind’) relative to ACC positions did not show a comparable increase (Figure 3E).

Hmox1-positive microglia target apoptotic RGCs through a PANX-1-purinergic signalling axis

To investigate these findings further, we specifically interrogated a subset of Hmox1-expressing microglia. This population has been previously implicated in vascular development and remodelling, particularly in the context of retinal maturation (Martineau et al., 2026). This subset of microglia exhibits a similar annular expansion pattern during the first postnatal week to that of the ACCs and SVP (Figure 4A). These microglia straddle the vascular margin, extending processes both ahead of and behind the vessels. In these regions, they maintain direct contact with ACCs and exhibit clear evidence of engulfment, with ACC material localised within intracellular vesicles (Figure 4C).

Figure 4
Download asset Open asset
Auto-fluorescent cluster complexes (ACCs) are composed of dying retinal ganglion cells (RGCs) engulfed by Hmox1-expressing microglia.

(A) Mouse retinal wholemounts displaying a subtype of microglia labelled by Hmox1, which sit as an annulus astride the ACC locations and the superficial vascular plexus (SVP). (B) Mouse retinal wholemounts displaying apoptotic cells labelled with YO-PRO-1 in the periphery and overlapping with the microglia annulus. Scale bar=1 mm. (C) Example Hmox-1-positive microglia with ACCs present inside intracellular vacuoles responsible for breaking down and storing apoptotic cells. (D) Apoptotic cells labelled with YO-PRO-1 are surrounded by Hmox-1 ramified microglia which become activated and engulf dying cells storing them in intracellular vacuoles similar to AAC storage. (E) Apoptotic cells labelled with YO-PRO-1 show complete overlap with RGC marker RBPMS. (F) Apoptotic cells labelled with YO-PRO-1 show no co-localisation with starburst amacrine cell (SAC) marker ChAT. (G) PANX-1 hemichannels location tightly overlaps with a cluster of YO-PRO-1-positive cells. (H) Higher-magnification inset of G displaying the tight association between YO-PRO-1-positive cells and PANX-1 hemichannels. Scale bar=50 µm.

Microglial activation is frequently driven by the sensing of environmental purinergic molecules. Specifically, apoptotic cells release ATP via voltage-gated pannexin-1 (PANX-1) hemichannels, which is sensed by microglial P2Y12 receptors. To determine if this signalling axis contributes to ACC formation, we utilised the apoptotic marker YO-PRO-1, which selectively enters cells through open PANX-1 channels. Wholemount staining revealed a broad centro-peripheral gradient of apoptosis; however, this apoptotic annulus was positioned more peripherally than the ACCs, SVP, and Hmox1-positive microglia (Figure 4B). This suggests that dying RGCs are apparent across the wider periphery of the retina, which are then contacted by the time-lagging annulus of Hmox1-positive microglia.

High-resolution immunohistochemistry further demonstrated that these apoptotic cells localise within the same intracellular vesicles as the ACCs (Figure 4C, D). Co-staining with Rbpms and ChAT confirmed the identity of these YO-PRO-1-positive cells as RGCs rather than SACs (Figure 4E, F). Finally, we observed a precise spatial alignment between PANX1 expression and YO-PRO-1-positive cells, which together form an ACC-like organisation in the developing retina (Figure 4G, H). Together this suggests that RGCs initiate apoptotic mechanisms, which then attract the attention of phagocytotic Hmox1-positive microglia, which then move to engulf the cells.

Purinergic signalling modulation drastically affects Hmox1 microglia activation and ACC formation

Hmox1 microglia activation is mediated via purinergic signalling through PANX-1 hemichannels in apoptotic cells, acting as a ‘find me, eat me’ signal. This ATP sensing is carried out by P2Y12 receptors on the microglial cell surface. To investigate and verify this fully, we examined the effects of blocking either ATP release through the PANX-1 channels with probenecid or ATP sensing from microglia via P2Y12 receptors with PSB-0739. Overnight incubation of P4 retinal wholemounts with probenecid or PSB-0739 had dramatic effects on Hmox1 microglial activation across all morphometric parameters. Blocking either purinergic release or sensing shifted Hmox1 microglia into a significantly more ramified or less activated state. This in short significantly decreased circularity, increased perimeter, increased total skeleton length, and number of branch points (Figure 5A–D). These changes in morphology are visually apparent in Figure 5J–L.

Figure 5 with 1 supplement see all
Download asset Open asset
Purinergic signalling through PANX-1 hemichannels and P2Y12 receptors influences Hmox1 microglia activation at postnatal day (P)4.

Quantification of microglial morphology at P4 under control conditions (black bars) and following pharmacological inhibition using probenecid 1 mM (darkest grey), probenecid 2 mM (medium grey), and PSB0739 5 μM (light grey) for 24 hr. Statistical analysis was conducted using one-way ANOVA followed by Tukey’s multiple comparisons test. Data are presented as mean ± SEM; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. n = 8–10 retinas per group. Cells per group: control = 6829, probenecid 1 mM = 5401, probenecid 2 mM = 4266, and PSB0739 5 μM = 3099. (A) Circularity was significantly reduced in probenecid 2 mM and PSB0739 5 μM compared to control. (B) Probenecid and PSB0739 in general significantly increased the number of branch points compared to control. (C) Cell perimeter was significantly increased by probenecid 2 mM and PSB0739 5 μM relative to control. (D) Total skeleton length was significantly higher in probenecid and PSB0739 5 μM compared to control. (E) Total number of Hmox1-positive cells observed after incubation of probenecid (1 mM or 2 mM) or PSB0739 5 μM for 24 hr prior to fixation and immunolabelling. (F) Total number of YO-PRO-1-positive cells observed after incubation of probenecid (1 mM or 2 mM) or PSB0739 5 μM for 24 hr prior to fixation and immunolabelling. (G) Total number of double positive YO-PRO-1 and Hmox1 cells observed after incubation of probenecid (1 mM or 2 mM) or PSB0739 5 μM for 24 hr prior to fixation and immunolabelling. (H) Percentage of Hmox1+ cells that co-localise with YO-PRO-1 signal after incubation of probenecid (1 mM or 2 mM) or PSB0739 5 μM for 24 hr prior to fixation and immunolabelling. (I) Percentage of YO-PRO-1-positive cells that express Hmox1, representing the proportion of apoptotic cells associated with Hmox1-positive microglia after incubation of probenecid (1 mM or 2 mM) or PSB0739 5 μM for 24 hr prior to fixation and immunolabelling. (J) Micrograph displaying overlap of apoptotic cells labelled with YO-PRO-1 and HMOX-1-positive microglia with higher magnification inset indicated by asterisk. (K) Micrograph displaying reduced overlap of apoptotic cells labelled with YO-PRO-1 and HMOX-1-positive microglia after 24 hr of incubation with probenecid 2 mM with higher-magnification inset indicated by asterisk. (L) Micrograph displaying reduced overlap of apoptotic cells labelled with YO-PRO-1 and HMOX-1-positive microglia after 24 hr of incubation with PSB0739 5 μM, with higher-magnification inset indicated by asterisk. Scale bar = 100 µm. Inset scale bar = 20 µm. (M) Top: Cumulative frequency distributions of apoptotic cells D1/D2 ratio. N=3344. Bottom: Cumulative frequency distributions of apoptotic cells: D1/D3 ratio. Key: Control (black), probenecid-treated (red), PSB0739-treated (blue). N=3344. (N) Top: Representative image of P4 mouse retinal whole mounts showing the SVP (IB4, red) and apoptotic cells (YO-PRO-1, green) across the centre-peripheral axis gradient from left (ONH region) to right (vascular leading edge). Middle: Retina incubated in probenecid for 24 hr (1 mM). Bottom: Retina incubated in PSB0739 (5 μM) for 24 hr. Scale bars = 100 μm.

Figure 5—source data 1

Microglia morphometric parameters across different drug conditions.

https://cdn.elifesciences.org/articles/111419/elife-111419-fig5-data1-v1.xlsx

Blockade of purinergic signalling also had a noticeable effect on the numbers of Hmox1 microglia and YO-PRO-1-positive cells. The number of Hmox1 microglia found in the retina increased across the board, although only probenecid 2 mM displayed a significant increase (Figure 5E). Unsurprisingly, blocking release or uptake of ATP drastically reduced the number of YO-PRO-1-positive cells, but with probenecid treatment, this may be a mechanistic effect of blocking the PANX-1 channels (Figure 5F). Similarly, the number and percentage of Hmox1 microglia containing apoptotic cell fragments in their vesicles also decreased after blockade. However, this only reached significance with probenecid treatment (Figure 5G, H). Blocking purinergic signalling did not seem to have any effect on the percentage of YO-PRO-1-positive cells, which were co-localised with Hmox1 microglia (Figure 5I).

In addition to morphometrics and cell frequency changes, we also examined the peripherality of YO-PRO-1-positive cells after drug application. To normalise the apoptotic cell locations to account for the changing retina size, we converted the origin locations into the D1/D2 metrics. These metrics showed no significant changes but there was a trend for probenecid-treated retinas to display a broader apoptotic distribution, with a shift towards the retinal periphery. On the other hand, D1/D3 metrics displayed a significant shift of the YO-PRO-1-positive cells treated with PSB-0739 away from the SVP edge and the peripheral non-vascularised areas towards the central retina (Figure 5M, N).

Stage II retinal waves display centrifugal expansion and modulation by purinergic signalling

Because ACCs are present exclusively during the period of stage II retinal waves, we hypothesised a link between these clusters and wave generation or propagation. To test this, we performed wholemount calcium imaging using the bath-loaded cell-permeable dye Calbryte 520AM on IB4-labelled retinas with application of the PANX-1 blocker probenecid, to map wave origination points and quantify wave metrics relative to the vasculature. Representative calcium imaging and wave activity plots are shown in Figure 6A, B. Given the tight anatomical association between ACCs and apoptotic cells expressing PANX-1 hemichannels, we next investigated whether blocking purinergic release altered wave dynamics.

Figure 6
Download asset Open asset
Centrifugal progression of retinal wave origination is driven by PANX1-mediated purinergic signalling.

(A) Calcium retinal waves plots of waves in control conditions. Each row represents a pixel region of interest (ROI) (10 µm × 10 µm) within the calcium imaging recording. Waves are indicated by synchronised activity across channels. The activity is shown against time (in seconds). (B) Probenecid 1 mM profoundly reduces retinal excitability, with significant decrease in wave frequency and number of channels recruited within waves. (C) Retinal wave frequency increases from postnatal day (P)3–6 while ATP release blockade with probenecid reduces frequency across the board. (D) Wave retinal area coverage is reduced significantly by probenecid treatment. (E) Retinal wave propagation speed is reduced by probenecid treatment in the later stage of first postnatal week. (F) Retinal wave path length increases from P3 to P5 and is significantly reduced by probenecid treatment. Statistical analysis was conducted using Mann-Whitney rank-sum test. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. (G) Mouse retinal wholemount (P4) displaying superior vascular plexus (SVP) (labelled with isolectin B4 [IB4]) and the origin points of spontaneous retinal waves recorded with calcium imaging (green spots). (H) Retinal wave origination becomes increasingly more peripheral (D1/D2>0.5) over the P3–6 period. (I) Example propagation extent of a retinal wave. (J) Retinal waves originate in the non-vascularised peripheral regions (D1/D3>1) of the retina, regardless of age.

Figure 6—source data 1

Calcium retinal wave metrics in control and probenecid conditions.

https://cdn.elifesciences.org/articles/111419/elife-111419-fig6-data1-v1.xlsx

Treatment with 1 mM probenecid profoundly attenuated nearly all measured wave metrics. Specifically, wave frequency (Figure 6C), speed (Figure 6E), and total path length (Figure 6F) were significantly reduced upon drug application. While probenecid treatment led to a significant decrease in wave area at P3, P5, and P6, P4 retinas uniquely exhibited a significant increase in this metric (Figure 6D).

By aligning functional activity with anatomical landmarks, we mapped all wave origination points relative to the SVP in the P4 retina (Figure 6G) and visualised the spatial coverage of individual waves (Figure 6I). Across all postnatal ages, the majority of retinal waves originated in the peripheral regions. These wave initiation points exhibited a centrifugal expansion pattern mirroring the progression of the ACCs, vasculature, and microglia. To best represent this, we calculated the distance from the ONH to the object (D1) and through to the retinal periphery (D2). The D1/D2 ratio metric approaches 1 when the relative distance between the wave origin point is close to the edge retina, regardless of physical size. We also created a version of the ratio, which is relative to the SVP border position. Accordingly, a ratio of D1/D3 ≤ 1 defined an object within the vascularised area, while D1/D3>1 indicated a position within the non-vascularised region (see Figure 1—figure supplement 1 for details).

Between P3 and P6, wave origins demonstrated a significant increase in relative peripherality (D1/D2; Figure 6H). This peripheral bias was even more pronounced when origins were normalised to the vascular margin; wave initiation was significantly more likely to occur within non-vascularised regions across all stages (Figure 6J). Indeed, the median D1/D3 value, representing the position relative to the vascular front, only approached 1 at P6, when the retina is nearly fully vascularised.

Synchronised centrifugal expansion pattern across multiple developmental processes

To examine the overall developmental expansion pattern, we extended the use of the D1/D2 metrics across retinal wave generation, vascular growth, apoptotic activity, and microglial positioning. Visualising the relative positions of these processes highlights a strongly conserved organisation (Figure 7). Spontaneous wave initiation points, recorded via microelectrode array (MEA) (orange) (see Figure 7—figure supplement 1 for details) or calcium imaging (cyan), predominantly emerge within the non-vascularised periphery and shift further towards the retinal margin between P3 and P6. This centrifugal progression converges at P6 as the SVP achieves near-total coverage. Similarly, the density of apoptotic cells (YO-PRO-1; blue) shifts peripherally over this period. Notably, the expansion of the SVP (magenta) consistently lags behind the wave initiation front, while the Hmox1+ microglial annulus (red) straddles both the vascular border and the ACC positions. Throughout development, ACCs maintain the most central position relative to these expanding fronts until P6, when they eventually reach the retinal periphery (brown).

Figure 7 with 1 supplement see all
Download asset Open asset
Relative peripherality of multiple developmental processes is age-invariant.

(A) Box plot showing developmental changes in D1/D2 peripherality ratios for stage II retinal waves recorded by microelectrode array (MEA) and calcium imaging, SVP extent, YO-PRO-1 labelling, HMOX-1 microglia locations, and auto-fluorescent cluster complexes (ACCs). Each box illustrates the median (horizontal line) and interquartile range, with minimum and maximum values (whiskers). The distribution of each marker of interest increases in peripherality (D1/D2>0.5) over the postnatal day (P)3–6 period, see Figure 1—figure supplement 1. (B) Mean and standard deviation plots for each functional or anatomical measures expressed in terms of D1/D2 peripherality metrics across the P3–6 time period.

Figure 7—source data 1

D1/2 metrics for retinal waves, vascular plexus, apoptotic cells, microglia, and ACCs.

https://cdn.elifesciences.org/articles/111419/elife-111419-fig7-data1-v1.xlsx

In summary, these data reveal a precisely orchestrated spatiotemporal sequence in the developing retina, where a peripheral wave of neuronal activity and RGC apoptosis consistently precedes vascular expansion. The stable spatial relationship between Hmox1-positive microglia, ACCs, and the trailing vascular front suggests that the outward migration of the SVP is not a stochastic process, but rather one guided by a pre-patterned landscape of purinergic signalling and phagocytic remodelling. This ‘wavefront’ architecture ensures that metabolic demand and neuronal maturation are tightly coupled with the emerging blood supply during the first postnatal week.

Discussion

Herein, we demonstrate for the first time clusters of auto-fluorescent cell complexes which are found in mouse retina over the first postnatal week. Investigating their nature and composition has revealed a tightly regulated synchrony of multiple developmental processes and illuminated several new insights in retinal development and angiogenesis (Figure 8A–C). Dying RGCs in the periphery release purinergic molecules, which help initiate retinal waves and act as a proangiogenic marker, which in turn helps guide the outward expansion of the vascular plexus. This vascular wavefront is coordinated by a distinct microglial annulus that actively monitors the retinal environment (Figure 8B). Upon contact, these microglia initiate the phagocytic clearance of apoptotic RGCs, resulting in the formation of ACCs, a process that effectively pre-patterns the retinal landscape for subsequent vascular expansion (Figure 8C).

Figure 8
Download asset Open asset
Overview of synchronised development in retina.

(A) Diagram of a retina at two different timepoints displaying the organisation of apoptotic retinal ganglion cells (RGCs), the superficial vascular plexus (SVP), the annulus of Hmox1 microglia, and auto-fluorescent cluster complexes (ACCs). Arrows indicate areas explored in higher detail in B, C. (B) Pro-angiogenic and spontaneous activity phase of the apoptotic RGCs releasing purinergic markers through PANX-1 hemichannels. (C) Phagocytic phase where Hmox1 microglia sense apoptotic RGCs and engulf them for destruction, creating ACCs.

More specifically, we have uncovered a developmental gradient that expands in a centrifugal manner from the ONH. We discovered that apoptosis of developing RGCs promotes the functional upregulation of PANX-1 hemichannels. These dying cells release a host of purinergic molecules such as ATP through the PANX-1 hemichannels. ATP acts to promote the initiation of retinal waves; blocking these PANX-1 channels greatly reduces the frequency and size of the waves. The apoptotic cells and wave initiations are more prevalent in the peripheral non-vascularised zones of the retina. The purinergic release and energy demand of wave initiations, in turn, act as a proangiogenic signal which drives the expansion of the SVP. A specialised subset of microglia expressing Hmox1 site astride the SVP edge and help guide the growth of endothelial tip cells. They also have a strong phagocytotic role, where they engulf apoptotic RGCs under the advancing vascularised area. Our findings indicate that microglia engulf apoptotic RGCs and sequester them within phagocytic vacuoles for degradation, thereby forming the ACCs that initially prompted this investigation. This model is further supported by our observation that vascular maturation is significantly more advanced in retinal regions where ACCs are present, suggesting these aggregates serve as a focal point for angiogenesis.

It is surprising that ACCs have remained unidentified until now, given their highly stereotyped progression pattern. However, well-documented auto-fluorescence from cellular debris and molecules such as lipofuscin (Kennedy et al., 1995; Sparrow, 2007) and drusen (Imamura et al., 2006; Spaide and Curcio, 2010) likely confounded previous attempts at identification. Their relative sparsity may have further led to their misclassification as non-specific staining, underscoring the necessity of pan-retinal visualization for reliable detection. Furthermore, their intrinsic auto-fluorescence complicates traditional immunohistochemical characterization. By leveraging scRNA-seq, we bypassed these optical limitations to reveal the molecular identity of ACCs as multicellular aggregates comprising microglia, apoptotic RGCs, and endothelial components.

The transcriptomic profile of the largest cell cluster (cluster 0) identifies these microglia as central orchestrators of postnatal retinal development. High expression of canonical markers, including Aif1, P2ry12, C1qb, and Sall1, reflects a specialised homeostatic state geared towards neurovascular interaction and phagocytic refinement. Specifically, the co-expression of microglial markers with RGC-specific Rbpms (Rodriguez et al., 2014) and apoptotic regulators (Bax) provides molecular evidence for the active clearance of RGCs (Edlich et al., 2011; Han et al., 2001). This process is likely driven by a P2ry12-mediated purinergic response to apoptotic signals and local hypoxia, with Aif1 (Iba-1) facilitating the necessary phagocytic cup formation.

Beyond debris clearance, the enrichment of C1qb and Sparc suggests these cells are actively involved in activity-dependent synaptic tagging and migratory modulation, respectively. Meanwhile, the maintenance of Sall1 expression indicates that while these microglia are highly active in remodelling, they retain a non-reactive, developmental identity. Collectively, these data support a model where Cluster 0 microglia integrate purinergic sensing and complement-mediated refinement to facilitate the precise spatiotemporal maturation of the retinal landscape.

An interesting finding of this research was that the Rbpms+ cells did not form a separate cluster within the transcriptomics data. This may be due to the fact that phagocytic microglia can encapsulate transcripts originating from neurons and other neural cell types, which are detectable by RNA-seq despite not belonging to a common microglial genetic signature. For example, Solga et al. showed that CNS microglia contain neuronal and oligodendrocyte-specific mRNAs that localise within microglia but are not translated. This research group interpreted that this RNA is acquired through phagocytosis or macropinocytosis of surrounding neural cells (Solga et al., 2015). Similarly, in zebrafish, synapse-engulfing microglia identified in situ display neuronal and synaptic gene expression in scRNA-seq profiles, consistent with engulfed neuronal material contributing to the detected transcriptome (Silva et al., 2021). In line with these observations and given our FACS strategy enriching autofluorescent ACCs rather than intact RGCs, we interpret the widespread Rbpms expression across ACC-associated clusters as an expected consequence of microglial engulfment of apoptotic RGCs, rather than evidence for a distinct population of viable RGCs that failed to form a separate cluster.

However, it must be noted that this study was unable to directly compare the transcriptomics of ACC+ cells versus ACC– cells. Due to the rarity of the ACC+ population, we focused on collecting and analysing a large sample of these data. Head-to-head comparison with ACC– data would greatly inform our understanding of the genetic makeup of the different populations.

A significant outcome of this study is the discovery that stage II retinal wave initiation follows a highly stereotyped, centrifugal expansion pattern. This observation stands in sharp contrast to the prevailing consensus in the field, which has historically characterised wave initiation as a stochastic or random process (Blankenship and Feller, 2010; Firth et al., 2005; Maccione et al., 2014; Stafford et al., 2009; Zheng et al., 2006). Notably, our data indicate that wave origins are consistently biased towards non-vascularised peripheral regions throughout the first postnatal week. While these findings refuted our initial hypothesis that ACCs serve as the primary source of wave initiation, it reveals a previously unrecognised topographical coordination between neuronal activity and retinal maturation. This spatial bias suggests that the ‘active’ zone of the developing retina is defined by the absence of vasculature, potentially linking wave dynamics to local metabolic or purinergic gradients.

Wave-like activity from the retina propagates to the rest of the visual system and is known to help form the proper retinotopy or world view in both V1 and the superior colliculus (SC) (Ackman et al., 2012; Blankenship and Feller, 2010; Firth et al., 2005). Previous in vitro retinal MEA recordings have shown that there is a propagation bias for retinal waves to travel along the nasal-temporal axis (Stafford et al., 2009). This direction bias is maintained through the SC and V1 (Ackman et al., 2012). However, none of these studies noted anything that resembled the centrifugal progression pattern described here. It follows that the centrifugal expansion of wave initiations may aid in the progressive development of the entire visual processing circuit throughout the brain. Focusing the retinal activity origins in concentric annuli ensures that the visual system develops from the central to peripheral retina representations. It follows that this pattern of progression may have a large effect on the development of the retinotopic map in the higher areas but that is beyond the scope of this research.

Several methodological factors likely explain why these developmental mechanisms remained previously undetected. First, our use of high-density MEAs provided superior spatial sampling and comprehensive pan-retinal coverage compared to the sparser configurations used in earlier studies. This was complemented by wide-field calcium imaging, which enabled us to localise wave dynamics with micron-scale precision and directly correlate them with the advancing SVP margin. Furthermore, while previous research often pooled data across the first postnatal week, thereby masking developmental shifts, our daily longitudinal analysis preserved the fine-tuned organisation of wave initiation. By avoiding the loss of temporal resolution inherent in data aggregation, we were able to resolve the centrifugal progression that is otherwise collapsed in cross-sectional or pooled datasets.

A defining feature of this research is the remarkable synchrony observed between neuronal activity, programmed cell death, and angiogenesis. By utilising normalised peripherality metrics (D1/D2), we demonstrated that these processes are not merely concurrent but are strictly organised into a mobile ‘wavefront’ that traverses the retina. Throughout the first postnatal week, retinal wave initiation is consistently sequestered within the non-vascularised periphery, shifting further towards the retinal margin in lockstep with the expanding vasculature.

This advancing front is preceded by an annulus of apoptotic RGCs (YO-PRO-1+), the majority of which reside immediately ahead of the SVP. Positioned precisely at this interface, a specialised subset of Hmox1+ microglia straddles the leading edge of the vascular plexus. These microglia actively engulf the dying RGCs, sequestering them within intracellular vacuoles to form the ACCs. Our genetic and histological evidence confirms that ACCs are the direct product of this phagocytic interaction, located strategically beneath the growing vascular margin. This highly ordered spatial hierarchy persists until P6, at which point the developmental annuli converge at the retinal periphery, signalling the completion of this primary maturation phase.

Our findings highlighted the importance of purinergic signalling through PANX-1 hemichannels in the integrated development of the retina. Modulation of purinergic signalling has been shown to drastically affect retinal wave dynamics (Stellwagen et al., 1999), but its source was not identified. From our work it seems likely that apoptotic RGCs are a great candidate for this modulation. Purinergic molecules are released through PANX-1 hemichannels, which are found in high levels on mature RGCs (Bao et al., 2004; Dvoriantchikova et al., 2006). Sensing extracellular purinergic markers through P2 receptors is the major molecule responsible for transitions from quiescent to activated phagocytotic microglia (Fontainhas et al., 2011; Velasquez and Eugenin, 2014). However, the differential effects of PANX-1 blockade on wave dynamics across the P3–6 period suggest that there may be other interactions along the developmental timeline which bias the effects observed. Blockade in younger animals affected wave frequency significantly with no effect on propagation speed. As the retina develops more, the blockade of PANX-1 does trend to reduce frequency but significantly reduce other measures such as total path length and wave propagation speed. This could reflect a reduction in overall PANX-1 expression with ageing (Ray et al., 2005) or a shift in the role of PANX-1 signalling from wave generation to wave modulation (Sernagor et al., 2003).

In order to fully understand the developmental synchrony uncovered in this work, more direct mechanistic investigation is required. Future work in this area should utilise chronic knockout lines for apoptosis (BAX-KO) or pannexin-1 to characterise disruptions to the normal developmental timeline. In addition, a more fine-scaled examination of the Hmox1-positive microglia’s role in guidance of the SVP outgrowth and digestion of apoptotic RGCs into ACCs would be beneficial.

However, the discovery of this integrated mechanism in the retina still raises the compelling possibility that a similar ‘wavefront’ governs development across the entire CNS. Auto-fluorescent inclusions within microglia, reminiscent of ACCs, have been observed in the ageing brain (Stillman et al., 2023), while purinergic signalling is already known to modulate spontaneous activity in the developing auditory system (Babola et al., 2021). Our findings suggest a universal developmental logic: the programmed over-proliferation of neurons creates a metabolic and purinergic gradient that directs vascular expansion, fine-tuned by specialised microglial activity.

This theory is bolstered by striking temporal correlations elsewhere in the CNS. In the mouse spinal cord, the peak of apoptosis at E12–13 aligns precisely with the invasion of vessels into deep tissue layers (Nakao et al., 1988; Yamamoto and Henderson, 1999). Similarly, cortical apoptosis at E14 coincides with vascular infiltration from the subventricular plexus (Chen et al., 2017; Gupta et al., 2021; Kuan et al., 2000). We propose that in these regions, Hmox1-positive microglia may perform the same ‘double duty’ observed in the retina: clearing superfluous apoptotic cells while simultaneously harnessing purinergic signals to guide angiogenesis and synchronise neural activity. Ultimately, our work highlights the necessity of a holistic, multi-system approach to resolve how neural activity and vascular architecture are co-constructed during development.

Methods

Animals

All experimental procedures were approved by the UK Home Office, Animals (Scientific Procedures) Act 1986. Experiments were performed on neonatal (P2–13) C57BL/6 mice. All animals were killed by cervical dislocation followed by enucleation.

Characterisation of auto-fluorescent cluster complexes

Cell dissociation

Dissected retinas from P5 C57BL/6 mice were kept in oxygenated artificial cerebrospinal fluid (aCSF) (118 mM NaCl, 25 mM NaHCO3, 1 mM NaH2PO4, 3 mM KCl, 1 mM MgCl2, 2 mM CaCl2, and 10 mM glucose) until they were dissociated using the Neurosphere Dissociation Kit (P) (Miltenyi Biotec GmbH, Teterow, Germany [103-095-943]), in accordance with the manufacturer’s instructions.

Fluorescence-activated cell sorting

FACS polypropylene collection tubes were pre-coated with 20% foetal bovine serum (FBS) overnight at 4°C prior to use. Dissociated cells were suspended in 2% FBS in the pre-coated tubes. 5% FBS collection buffer was added to a coated FACS collection tube just before FACS flow started. The pre-sorted tube was controlled at 4°C, and the collection tube was controlled at 5°C. Fluorescence-activated BD Aria Fusion with a 130 nm nozzle and 10 PSI pressure was used for cell sorting. Auto-fluorescence typically peaks between approximately 380 and 600 nm (ultraviolet to red light). Therefore, a combination of five laser fluorescent detectors was utilised to maximise yield. The auto-fluorescent cell population was sorted first by cells that were auto-fluorescent, then by area, and lastly by width.

scRNA-seq

Following FACS, the final cell suspension was centrifuged at 500×g for 10 min. All liquid was discarded from the top, leaving 30 µl (max volume able to be inserted into the 10x Genomics microfluidic chip). The remaining sample was loaded on the 10x Chromium controller for scRNA-seq conducted in accordance with the protocols established by 10x Genomics – Universal 3' Gene Expression (Chromium Next GEM Single Cell 3ʹ Kit v3.1, 4 rxns PN-1000269).

scRNA-seq was conducted by the Genomic Core Facility and the Bioinformatics Support Unit at Newcastle University, achieving a sequencing depth of 50,000 reads per cell through Illumina NovaSeq 6000. The binary base call (BCL) files underwent de-multiplexing through CellRanger mkfastq version 3.01 to create FASTQ files, followed by alignment and quantification against the human reference genome GRCh38 utilising CellRanger count. Quality control assessments were conducted for each sample in R, eliminating any cells with fewer than 1000 reads, fewer than 500 genes, or more than 10% mitochondrial reads. Additionally, cells expressing haemoglobin genes were excluded from the analysis. The identification of doublets was performed using DoubletFinder, and these were subsequently filtered out from the dataset. Seurat (version 4.3.0) was then used for downstream analysis. The data was normalised and scaled to reduce intercellular technical variation and account for differences in gene expression levels, with nFeature_RNA, percent.mt, and nCount_RNA regressed out during scaling. Principal component analysis was then performed using the 2000 most highly variable genes, followed by graph-based Louvain clustering. Retinal cells were subsequently extracted from the individual samples, and batch effects were mitigated using Harmony (version 0.1.1) to generate a unified dataset. The data visualisation was performed through UMAP.

Cluster analysis

QIAGEN Ingenuity Pathway Analysis (IPA) software was used for in-depth examination of the differentially expressed gene lists derived from RNA sequencing via IPA core analysis. IPA synthesises meticulously curated information from a variety of biological data sources, including gene expression, molecular interactions, and pathway data from a vast experimental database. This synthesis facilitates the identification of crucial biological signals. The outcomes of the differential expression analysis were refined to retain only those with a p_val_adjust of 0.05 or lower for each cell type, subsequently uploaded to IPA. The gene symbols were designated as ID with the selection of the ‘Gene symbol’ option. The avg_log2FC and p_val_adj were utilised as the ‘Observations’, with ‘Exprs Fold Change’ and ‘Exprs False Discovery Rate’ options applied, respectively. Next, the core analysis was conducted by selecting the ‘Expression Analysis’ option, with ‘Exprs Fold Change’ chosen to compute the z-score. This analysis was performed by utilising the ‘Ingenuity knowledge base (genes only)’, taking into account both ‘Direct and indirect’ relationships, and ‘Causal networks’ were selected. Following the completion of the core analysis, the ‘Upstream regulators’ option was selected, and the results were organised by z-score to identify the networks exhibiting the highest activation scores.

Electrophysiology methods

Retinas were isolated from mouse pups at P2 (N=4 retinas), P3 (N=4), P4 (N=10), P5 (N=14), P6 (N=3), P7 (N=2), P8 (N=2), P9 (N=2), P10 (N=2), P11 (N=2), P12 (N=1), P13 (N=1). The isolated retina was placed, RGC layer facing down, onto the MEA and maintained stable by placing a small piece of polyester membrane filter (Sterlitech, Kent, WA, USA) on the retina followed by a bespoke anchor. The retina was kept in constant darkness at 32°C with an in-line heater (Warner Instruments, Hamden, CT, USA) and continuously perfused using a peristaltic pump (~1 ml/min) with aCSF equilibrated with 95% O2 and 5% CO2. Retinas rested in situ for 2 hr before recording, allowing sufficient time for spontaneous activity to reach steady-state levels. Probenecid (4-(dipropylsulfamoyl)benzoic acid) (water-soluble form, Thermo Fisher Scientific, P36400) was used at 1 mM.

High-density MEA extracellular recordings of spontaneous waves were performed as described in detail in Maccione et al., 2014, using the BioCam4096 platform with APS MEA chips type HD-MEA Stimulo (3Brain GmbH, Switzerland), providing 4096 square microelectrodes of 21 µm×21 µm in size on an active area of 5.12×5.12 mm2, with an electrode pitch of 81 µm. Two P5 and one P4 datasets were acquired with the MEA chip HD-MEA Arena (2.67×2.67 mm2 active area, electrode pitch 42 µm) for electrical imaging.

Raw signals were visualised and recorded at 7 kHz sampling rate with BrainWaveX (3Brain GmbH, Switzerland). Each dataset consisted of 30 min of continuous recording of retinal waves. Following the recording session, retinas were photographed on the MEA to document their precise orientation relative to the electrode array.

Calcium imaging methods

Tissue preparation

C57BL/6 pups from P3 (N=8), P4 (N=15), P5 (N=11), and P6 (N=8) were enucleated, and retinas were dissected at room temperature in oxygenated aCSF under a dissecting microscope. Isolated retinas were mounted whole onto a hydrophilic PTFE membrane (H100A047A Advantec, Japan) with the RGC layer facing upwards. All further incubations were conducted at 35°C in a hyperoxygenated darkened chamber. P4 and older retinas underwent incubation in 0.0001% Pronase E (P5147 Sigma-Aldrich) for 30 min to permeabilise the inner limiting membrane (Dalkara et al., 2009). To label the extent of vascularisation, the retinas were incubated in isolectin B4 594 (DL-1208, Vector Labs) for 30 min at 10 µg/ml (Stucky and Lewin, 1999). Retinas were bath-loaded with the calcium indicator Cal 520 AM (AAT Bioquest) (10 µM) for 2 hr and were then flattened further with a second piece of PTFE membrane placed on top and before transferring to the imaging microscope. The retinas were perfused with fresh oxygenated aCSF with a peristaltic pump at 1.7 ml/min. Tissue was allowed to settle in the perfused heated solution for at least 30 min before imaging.

Data acquisition

Imaging was conducted in the Newcastle University Functional and Light-Activatable Multi-dimensional Electrophysiology facility (FLAME). This was conducted on a Nikon FN1 microscope at ×4 NA 0.2 (Nikon CFI Plan Apochromat Lambda D ×4) magnification with a ×0.63 relay lens resulting in a field of view of 5.3×5.3 mm2 and a resolution of 2.6 µm per pixel. To enable multiple wavelength imaging, the microscope used a quad band Chroma 89402 filter cube. Images were acquired at 470 nm and 590 nm for the calcium signal and blood vessels, respectively. Time series images were acquired at 2 Hz for 10 min at 2048×2048 pixels at 16-bit resolution. For drug applications, an appropriate wash-in and wash-out time was used according to the literature. Probenecid (1 mM) was used to block purinergic release from apoptotic cells and examine the effect on retinal wave generation/spread.

Data processing and analysis

All data processing was conducted in MATLAB. Image time series were motion-corrected using a 2D cross-correlation approach. To properly visualise the retinal waves, the time series were downsampled to 512×512 pixels and went through a pixel-wise ΔF/F procedure. The calcium activity baseline was calculated by creating a low-pass percentage filtered trace. Each movie frame (F) was normalised by dividing its difference from the baseline frame (F – F0) by the baseline frame ((F – F0)/F0) to produce a ΔF/F movie.

To track the spatiotemporal properties of the calcium waves, the ΔF/F movies were thresholded, and the waves were identified with a semi-automated algorithm to determine their identity. Once this was done, a large variety of metrics were extracted, including initiation point, speed, area, frequency, and spatial relation to vascularised area.

All code relating to this project can be found here: https://github.com/GrimmSnark/paperCodeSavage2026, copy archived at Savage, 2026.

Immunohistochemistry

Immunostaining

Wholemount retinas were prepared from mouse pups aged P2–11, flattened on nitrocellulose membrane filters or PTFE membrane and fixed for 45 min in 4% PFA. Some retinas were labelled for apoptotic cells with the selective YO-PRO-1 dye (Y3603, Invitrogen) at 200 µM for 1 hr in carbogenated aCSF before fixation. Retinas were incubated with the primary antibodies (RBPMS 1830-RBPMS, rabbit polyclonal, phosphosolutions [1:500], ChAT AB144P, goat polyclonal, Merck Millipore [1:500], VChAT PA5-77386, rabbit polyclonal, Thermo Fisher Scientific [1:500], Iba-1 019-19741, rabbit polyclonal, Alpha Labs [1:1000], HMOX-1 AB_2118685, rabbit polyclonal, Proteintech [1:1000], PANX-1 488100, rabbit polyclonal, Invitrogen [1:500]) with 0.5% Triton X-100 in PBS for 3 days at 4°C, then washed with PBS and incubated with the secondary antibody solution for 1 day at 4°C (with or without IB4). Secondary antibodies included 0.5% Triton X-100 with donkey anti-rabbit Alexa 568 (1:500), donkey anti-goat Dylight 488 (1:500), goat anti-rabbit A546 (1:500). Isolectin B4 (IB4) staining of blood vessels (1:250) was performed together with the secondary antibody staining step. Finally, retinas were washed with PBS and embedded with Vectashield (H-1900, Vector Laboratories).

For a specific antibody lot of PANX-1, the tissue underwent an additional antigen retrieval step in a water bath at 80°C for 30 min using sodium citrate. Retinas were then incubated in blocking solution (5% secondary antibody host species serum with 0.5% Triton X-100 in PBS) for 1 hr.

Imaging

Zeiss AxioImager with Apotome and a Zeiss LSM 800 confocal microscope were used to image the retinas. High-resolution images of the RGC layer down to the INL were obtained by subdividing retinal wholemounts into adjacent smaller images that were subsequently stitched back together to view the entire retinal surface. ROIs were selected around the clusters.

To compensate for variability in retinal thickness, several focus points were set across the retinal surface to maintain sharp focus on the desired cell layer. Each picture was then acquired in all colour channels at ×20 magnification, and with 10% overlap between neighbouring areas. This overlap is used to correctly align and stitch together all pictures using the Zen Pro software (Zeiss). Some wide-field fluorescent images were acquired with the Nikon Ni-E microscope. Grids of 5×5–8×8 images were taken at 10× or 20×, and then combined to obtain a high-resolution image of the whole retina.

Peripherality and vascular relation metrics (D1/D2 and D1/D3)

To determine the relative position of objects, including ACCs, labelled cells, and wave origins, we calculated the distance from the ONH to the object (D1) and through to the retinal periphery (D2). The ratio (D1/D2) provides a measure of an object’s relative peripherality, independent of individual retinal size. Additionally, for a subset of samples, we measured the distance from the ONH through the object to the margin of the SVP (D3). Accordingly, a ratio of D1/D3≤1 defined an object within the vascularised area, while D1/D3>1 indicated a position in the non-vascularised region (see Figure 1—figure supplement 1 for details).

For the ACC D1/D2 values (Figure 1F), one-way ANOVA was used on all 233 ratio values for all eight groups. Tukey post hoc test was used to identify significant changes in cluster positions between consecutive developmental days.

Vascular plexus analysis

Sholl analysis was performed using Fiji to quantify blood vessel branches. IB4-stained vasculature images from retinal wholemounts were rendered binary using the ‘Threshold’ function. To remove signals inherent to auto-fluorescence of the ACCs, a separate colour channel image showing only cluster cells was rendered binary and subtracted from the vasculature image. Subtracted images were then adjusted using the ‘Brightness/Contrast’ function to obtain solid binary images.

To quantify blood vessel density in the vicinity of ACCs, segmental sections of the binary, cluster-subtracted whole retina vasculature images were outlined using the ‘Angle’ tool (allowing measurement of the angle of the segment and subsequent calculation of the surface area of the ROI) either with or without a cluster at the edge of the vascular plexus. Sholl rings were set from the centre of the ONH with a step size of 30 µm. Measurements were not taken from the inner 50% of the segment (based on the radius of the vascular plexus at its largest point). Intersections were counted manually.

To quantify blood vessel branch density in areas with ACCs versus areas without at matching eccentricities, ROIs were outlined using the ‘Polygons’ function either around clusters (ACC+ROI) or in adjacent ACC-negative regions at similar eccentricities (ACC – ROI). This was done in images from a colour channel in which the blood vessels were not visible. Once all ROIs were outlined for one retina, they were overlaid on the binary, cluster-subtracted vasculature image for that same retina. Any ROIs noticeably outside of the vascular plexus were discounted. Sholl rings were set from the centre of the ONH with a step size of 30 µm. Intersections were counted manually.

All cell counts were performed using Fiji. Cluster cells were counted manually in retinal wholemount images using the ‘Multi-point’ function. Cells were identified by auto-fluorescence using an image captured on a channel without any fluorescing immunostaining. The vascular plexus was outlined using the ‘Freehand’ tool, and ‘Measure’ function was used to determine surface area. Clusters were delimited and cells counted manually, identified by auto-fluorescence. A ‘cluster’ was defined as an uninterrupted group of auto-fluorescent cells spatially separated from any other group, localised near the outer edge of the SVP. No lower or upper size limit was assigned to clusters.

ACCs and microglial densities

To quantify microglia and ACC densities, radial straight lines were drawn from the centre of the ONH to the periphery of the retina, either through the centre of an ACC group or through an adjacent ACC-negative region. This was only performed where radial lines reached the periphery of the retina, uninterrupted by cuts. ROIs were outlined using the ‘Oval’ selection tool at four points along each radial line to comprise an ROI set. The four ROI types in each set were ahead of the ACC positive/negative region, at the ACC positive/negative region, behind the ACC positive/negative region, and close to the ONH. ROIs were outlined at comparable eccentricities for each set. Iba-1-stained microglia were counted manually. All Iba-1-stained whole retinas were from P5 or P6 animals. ACCs were identified by auto-fluorescence and counted manually.

Quantification of microglia, apoptotic cells, and co-localisation

For retinal wholemounts stained for HMOX-1 and YO-PRO-1, binary masks were created using Fiji/ImageJ, Labkit (Arzt et al., 2022), and custom MATLAB code (https://github.com/GrimmSnark/paperCodeSavage2026; copy archived at Savage, 2026). Labelled cells were counted and co-localisation percentages were also calculated. This was based on the HMOX-1 microglia mask containing any positively labelled pixels from the YO-PRO-1 channel. Statistical comparisons between different drug conditions were conducted using a Kruskal-Wallis ANOVA test with multiple comparisons, threshold at p=0.05.

Microglia morphometry

Computational microglia labelling

Images were processed and filtered using Fiji/ImageJ (Schindelin et al., 2012). Images were cleaned for background noise using Gaussian convoluted background subtraction (σ=50 pixels) and median filtering (3×3 pixels). Single colour image channels depicting stained cells of interest (YO-PRO-1, Hmox1, etc.) were segmented with Labkit (Arzt et al., 2022) and custom code (https://github.com/GrimmSnark/paperCodeSavage2026; copy archived at Savage, 2026). Quantitative measurements of the labelled microglia were conducted utilising the Fiji plugin ‘Measure Microglia Morphometry’ (Martinez et al., 2023). Briefly, this plugin quantifies a plethora of morphological metrics, including area, perimeter, convex area, average branch length, and branch point number (Figure 5—figure supplement 1).

Microglial behavioural parameters: statistical analysis

Statistical analysis and data visualisation were performed using GraphPad Prism 10. Two-tailed Mann-Whitney tests and non-parametric ANOVA were applied, with a significance threshold set at p≤0.05. Given the non-parametric nature of the data distribution, results were presented as the median accompanied by a 95% confidence interval (CI). The significance levels are indicated as follows: not significant (ns) for p>0.05, * for p≤0.05, ** for p≤0.01, *** for p≤0.001, and **** for p≤0.0001.

Data availability

We have uploaded the raw imaging data for example wide field calcium recordings of P3-6 which was used to produce Figures 6 and 7. We have also uploaded the raw data which was used to calculate retinal Multi-Electrode Array (MEA) activity used in Figure 7. All other raw imaging data and images are available upon request as they are too large to upload to Zenodo. Raw calcium imaging data totals 3 terabytes, microglial and IHC imaging totals 4 terabytes. These data reside on internal university servers which can be accessed via ftp. To gain access to the data please contact the corresponding authors with the details of your query. We have uploaded all sc-RNA gene data to GEO. All code related to the project is available here: https://github.com/GrimmSnark/paperCodeSavage2026, copy archived at Savage, 2026.

The following data sets were generated
    1. Cori Rhian B
    2. Majlinda L
    3. Rachel Q
    4. Evelyne S
    5. Michael S
    (2026) figshare
    Single cell RNA Sequencing of Autofluorescent Cluster Complexes in Mouse Neonatal Retina.
    https://doi.org/10.25405/data.ncl.29965943
    1. Cori Rhian B
    2. Majlinda L
    3. Rachel Q
    4. Evelyne S
    5. Michael S
    (2026) NCBI Gene Expression Omnibus
    ID GSE342498. Developmental Synchrony of Retinal Waves, Apoptosis, and Angiogenesis in Postnatal Retina.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE342498
    1. Savage M
    2. Bertram Cori R
    3. de Montigny J
    4. Thorne C
    5. Queen R
    6. Lako M
    7. Hilgen G
    8. Sernagor E
    (2026) Zenodo
    Developmental Synchrony of Retinal Waves, Apoptosis, and Angiogenesis in Postnatal Retina- Muli Electrode Array and wide field calicum imaging of P3-11 Retinas.
    https://doi.org/10.5281/zenodo.21873270

References

    1. Chan-Ling T
    2. Gock B
    3. Stone J
    (1995)
    The effect of oxygen on vasoformative cell division. Evidence that “physiological hypoxia” is the stimulus for normal retinal vasculogenesis
    Investigative Ophthalmology & Visual Science 36:1201–1214.
  42. Book
    1. Sparrow JR
    (2007) Lipofuscin of the retinal pigment epithelium
    In: Spaide JR, editors. Atlas of Fundus Autofluorescence Imaging. Berlin, Heidelberg: Springer. pp. 3–16.
    https://doi.org/10.1007/978-3-540-71994-6_1

Article and author information

Author details

  1. Michael A Savage

    Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration
    For correspondence
    michael.savage2@newcastle.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0001-2717-5412

  2. Cori Bertram

    Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0006-5488-0340

  3. Jean de Montigny

    Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    Present address
    Institute of Neuroscience of Montpellier, Inserm, France
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5832-5065

  4. Courtney A Thorne

    Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    Present address
    Faculty of Medical and Health Sciences, Medical Sciences, University of Auckland, Auckland, New Zealand
    Contribution
    Formal analysis, Investigation, Visualization
    Competing interests
    No competing interests declared

  5. Rachel Queen

    Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Formal analysis, Investigation, Visualization
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0414-2650

  6. Majlinda Lako

    Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1327-8573

  7. Gerrit Hilgen

    1. Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    2. School of Geography and Natural Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
    Contribution
    Supervision, Writing – review and editing
    For correspondence
    gerrit.hilgen@northumbria.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5008-5493

  8. Evelyne Sernagor

    Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration
    Competing interests
    No competing interests declared

Funding

Leverhulme Trust (RPG-2022-061)

  • Evelyne Sernagor

Biotechnology and Biological Sciences Research Council (BB/T017627/1)

  • Evelyne Sernagor

Engineering and Physical Sciences Research Council (EP/Y031016/1)

  • Majlinda Lako

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful to Leverhulme Trust (RPG-2022-061), BBSRC (BB/T017627/1), and EPSRC/ERC (EP/Y031016/1) for funding this work. We would also like to thank the Newcastle University FMS Bioimaging Unit for their continual support during this project. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. This work is dedicated to the memory of Evelyne Sernagor, who conceptualised and led this study until her passing in March 2025. Her brilliance and mentorship remain the foundation of this research.

Ethics

All experimental procedures were approved by the Animal Welfare Ethical Review Board (AWERB) of Newcastle University and carried out in accordance with the guidelines of the UK Home Office, under the control of the Animals (Scientific Procedures) Act 1986.

Version history

  1. Preprint posted:
  2. Sent for peer review:
  3. Reviewed Preprint version 1:
  4. Reviewed Preprint version 2:
  5. Version of Record published:

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.111419. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2026, Savage et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 645
    views
  • 34
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Michael A Savage
  2. Cori Bertram
  3. Jean de Montigny
  4. Courtney A Thorne
  5. Rachel Queen
  6. Majlinda Lako
  7. Gerrit Hilgen
  8. Evelyne Sernagor
(2026)
Developmental synchrony of retinal waves, apoptosis, and angiogenesis in postnatal retina
eLife 15:RP111419.
https://doi.org/10.7554/eLife.111419.3

Share this article

https://doi.org/10.7554/eLife.111419

Categories and tags

Research organism