The brain of the female Anopheles gambiae mosquito at spatial and single-cell resolution

Irene Z Rossi; Cécile Cassan; Johannes W Bagnoli; Sümeyye Özhan; Adeline Valente; Quentin J Delorme; Geert-Jan van Gemert; Chiara Andolina; Wouter Graumans; Richárd Bártfai; Avinash R Shenoy; David Carrasco; Emmanuel Perisse; Teun Bousema; Sarah H Merkling; Anna Cohuet; Arthur M Talman

doi:10.1038/s41467-026-76839-z

. 2026 Sep 17;17:10940. doi: 10.1038/s41467-026-76839-z

The brain of the female Anopheles gambiae mosquito at spatial and single-cell resolution

Irene Z Rossi ^1,^✉, Cécile Cassan ^1,^#, Johannes W Bagnoli ^1,^#, Sümeyye Özhan ^2,³, Adeline Valente ¹, Quentin J Delorme ¹, Geert-Jan van Gemert ⁴, Chiara Andolina ⁴, Wouter Graumans ⁴, Richárd Bártfai ⁵, Avinash R Shenoy ⁶, David Carrasco ¹, Emmanuel Perisse ⁷, Teun Bousema ⁴, Sarah H Merkling ², Anna Cohuet ^1,^✉, Arthur M Talman ^1,^✉

PMCID: PMC13680466 PMID: 42897131

Abstract

Blood feeding in Anopheles mosquitoes is at the root of all malaria infections in humans and is supported by a range of physiological and behavioral adaptations. Since behavior is orchestrated at the level of the central nervous system, we surveyed the cellular diversity and spatial organization of the female Anopheles gambiae brain and compared it to other insect species. We then describe transcriptional changes associated with mosquito life history events with single brain and single-cell RNA sequencing. We did not identify expansion of specific cell populations, but a modulation of neurotransmission in the mated mosquitoes, and a metabolic and immune response associated with glia, Kenyon cells and immune cells after bloodmeal, and detected signatures of aging in the mosquito brain. We did not find an effect of Plasmodium falciparum infection. Our atlas will empower the study of non-model insect neurobiology, neural basis of vector behavior, and mechanisms underlying malaria transmission.

Subject terms: Neuroscience, Microbiology, Systems biology

Mosquito behavior drives malaria transmission, yet the Anopheles brain remains largely uncharted. Here, authors show a single-cell and spatial atlas of the female An. gambiae brain and its transcriptional changes across key life-history events.

Introduction

The nervous system is responsible for perception and integration of both endogenous and environmental cues, enabling decision-making and the generation of adaptive physiological and behavioral responses. The cellular diversity supporting this network is almost uncharted in insects, and most of the knowledge on the insect brain has been derived from Drosophila melanogaster^1,2. In such a model system, cellular diversity can be interpreted in the light of decades of studies on cell lineages and developmental processes. However, highly diverse behaviors computed by specific cell networks are found among insect species^3,4.The organization and diversity of cell types in the brain of non-model organisms and insect vectors has just started to be explored and contrasted to model organisms by means of cell type taxonomy methods and cross-species cellular annotation^5–7. In these species, the progressive characterization of brain cell types is revealing the cellular basis of behavioral states. For instance, differences in Kenyon cell (KC) type composition and abundance in the mushroom bodies (MBs) are linked to behavioral states and social function in ants⁵. In bees, gene regulatory networks within defined KC populations underlie foraging behavior⁸, whereas in Aedes mosquitoes, blood feeding is accompanied by a massive transcriptional shift in glial cells⁷. These studies underscore the need for cell-resolved data for understanding how behavior emerges from neural transcriptional states.

In mosquitoes, blood feeding has evolved as an essential trait for nutrient acquisition, supporting egg development, but has also been exploited by pathogens as a means to gain access to new hosts. Anopheles females are the sole vectors of human Plasmodium parasites, and long-term ecological interactions have shaped their coevolution⁹. Plasmodium falciparum is also suspected to manipulate the host-seeking and biting behavior of its vector to enhance transmission success, albeit with conflicting evidence^10–12. Yet, in Anopheles, very little information exists on the structure and composition of the brain, in spite of the central role of behavior in determining vectorial capacity, and, prior to this study, molecular characterization was limited to only two general neuronal markers and a single glial marker^13–15. Here, we determine the cellular diversity of the female An. gambiae brain, spatially resolve the organization of the brain within mosquito heads, and establish transcriptional signatures of female mosquito aging and life history events associated with malaria parasite transmission. This approach allowed us to curate the first extensive annotation of the adult female Anopheles brain cell-type atlas and provide a comprehensive overview of the brain transcriptional processes associated with life-history traits.

Results

A cellular atlas of the female An. gambiae brain

We dissociated six pools of the brains of female An. gambiae mosquitoes (either virgin, mated or blood-fed), and analyzed them using single-cell RNA-seq (scRNA-seq) (10x Genomics) (Fig. S1A). After quality control, the An. gambiae brain atlas consisted of 48,806 high-quality cells (~20% of the expected number of cells in a single brain¹⁴), with an average of 5, 350 unique molecular identifiers (UMIs) and 1, 678 genes per cell (Fig. S2A–D). Using a conservative approach aimed at avoiding overclustering, we identified 45 cell clusters (hereafter Ag clusters). All cell clusters were present in all experimental replicates, indicating their consistent representation in the pools (Fig. S2E, F).

We first mapped the expression of orthologs of general cellular markers of neurons (multiple genes, among which the most expressed was elav-like) and glia (repo), as these markers are widely conserved across the animal kingdom^16,17, including in dipterans¹⁴. As expected, a large majority of cells expressed elav-like and lacked significant expression of repo, and were grouped in 40 neuronal cell clusters. In contrast, repo was expressed in a minority of cells belonging to three clusters (Fig. 1A, B). Additionally, two clusters were devoid of elav-like or repo expression, including Ag42, which expressed collagen type IV alpha (col4a) (Fig. 1A, B), an ortholog of the D. melanogaster marker (hereafter Dm markers) of hemocyte col4a1¹⁸. Neurotransmitter profiling identified widespread cholinergic and glutamatergic functions, while seven clusters were strongly GABAergic (Fig. 1B, Fig. S3A and Supplementary Data 1).

Fig. 1 — A Uniform Manifold Approximation and Projection (UMAP) of 48,806 high-quality single-cell transcriptomes (from three experimental conditions with two experimental replicates each, for a total of six samples, each derived from ten brains) in the atlas, highlighting expression levels of *elav-like*, *repo* and *col4a*. B Notable marker gene expression in the 45 clusters of the atlas (cluster ID on top), alongside a heatmap showing the strength of alignment of Ag clusters to *Ae. aegypti* (Aa), *D. melanogaster* (Dm), *M. pharaonis* (Mp), and *A. mellifera* (Am) cell types. Neural markers: *elav-like* (*AGAP000965*)*, AGAP001883, synaptotagmin-1, AGAP029448, nSyb, AGAP010286*, *poxn*; glial markers: *turtle, obp9, repo, tret1, lipocln*; immune cell markers: *scrb9, sparc, col4a*. C Example of Ag clusters top alignments to cell types of the multispecies SAMap panel. Alignment values > 0.4 are displayed with a star. D UMAP of the brain atlas displaying consensus annotation of the Ag clusters.

We next conducted a cross-species cell type mapping (SAMap)⁶ against a panel of six single-cell and single-nucleus RNA-seq datasets from brains of various insects, including three different datasets from the fruit fly D. melanogaster^19–21, as well as atlases from the mosquito Aedes aegypti⁷, the honeybee Apis mellifera⁸ and the ant Monomorium pharaonis⁵. The cross-species comparison revealed cell type matches consistent with the evolutionary divergence^22–24. Ag clusters had the highest similarity to Ae. aegypti clusters, followed by D. melanogaster and A. mellifera cell types, while they most differed from M. pharaonis ones (Fig. 1B, C). Most cell types were consistently matched across species (Fig. 1B, C and Fig. S3B, C, and Supplementary Data 2). We concomitantly identified marker genes for each cluster and genes with orthologs with known functions in other systems, and combined these results with the SAMap cell type assignment (Fig. 1D and Supplementary Data 1, 2). We leveraged cell type homology scores for the following cellular marker inspection. We observed a high concordance of our marker-based cell type assignment with the algorithm-predicted clusters (Fig. S3A, B and Supplementary Data 2, 3), and the analysis revealed some strong, consistent cross-species homologies (Fig. 1B, C). For instance, Ag41 was assigned with high confidence to Poxn neurons (Supplementary Data 2) and consistently was marked by expression of poxn²⁵ (Fig. 1B). Similarly, general neural, glial and immune cell identity was confirmed by both marker analysis and cross-species cell type homology (Fig. 1A–C, Fig. S3, and Supplementary Data 2). Interestingly, Ag38 specifically expressed scrb9, a univocal marker of An. gambiae oenocytoids in hemolymph cells²⁶, and did not have a consistent assignment to other insect species cell type (Fig. 1B, C). Altogether, our brain atlas provides the first comprehensive map of the cellular diversity in the An. gambiae female brain.

Spatial organization of the female An. gambiae head

We next sought to define the spatial organization of the brain within the head of An. gambiae female, as its neuroanatomy has not yet been fully investigated. We initially used confocal microscopy with a presynaptic staining on dissected brains to understand the anatomy of the An. gambiae brains, and to contrast it to other dipterans. In addition to canonical insect brain structures^15,27, including the optic lobes, the subesophageal zone and the central complex, the An. gambiae MB closely resembled that of D. melanogaster. It comprised conserved vertical and horizontal lobes that were further subdivided into distinct KC subclasses^28,29 and subcompartments, likely innervated by different neuromodulatory neuron types, such as dopaminergic neurons^30,31. We also identified notable differences relative to D. melanogaster. Although variation in the number of antennal lobe glomeruli (Fig. S4A) is common across insect species³², the organization of central complex substructures and the superior protocerebrum surrounding the MB vertical lobes³³ (Fig. S4B) appear to be distinctive features of the An. gambiae brain. We next performed histological sections followed by hematoxylin and eosin (H&E) staining, and observed hematoxylin-stained cell body rind regions as well as eosin-stained neuropils (Fig. 2A). Brain regions were more readily observed in the axial sections, and this orientation was selected for further experiments. We used RNAscope in situ hybridization (ISH) on head sections and observed a wide distribution of the neuronal marker elav-like in soma-rich regions of the brain (Fig. 2B), marking the optic lobe underlying the eye as well as deeper cell body rind regions. To systematically understand the composition of the brain, we complemented high-resolution ISH with a lower-resolution (10 µm), but untargeted, Slide-seq spatial transcriptomic dataset of 8 female heads from two arrays³⁴ (Fig. S5A). After quality control, we recovered 9,914 high-quality spatially-resolved transcriptomes (Fig. S5B), that could be grouped in 14 spatial clusters (hereafter Sp clusters) (Fig. S5C, D). We further subsetted, oriented, and aligned five high-quality head datasets in space using the consecutive H&E-stained slices (Fig. S5A). Using a consensus classification approach combining (i) marker gene analysis (Supplementary Data 2), (ii) mapping of the spatial clusters onto the cell atlas (Fig. S5E) with 66% of the spots matching to one or more single-cell clusters and 50% of which were assigned to a single-cell cluster, (iii) an inter-spatial cluster distance metric (Fig. S5F), and (iv) the spatial distribution of the Sp clusters (Fig. S6), we were able to assign a tissue and cell composition to most spatial clusters (Fig. 2C and Supplementary Data 2). Sp1 and Sp4 were associated with glia, while Sp10 was assigned a hemocyte ontology. Neurons were grouped in Sp2 and Sp12, marked by expression of elav-like and situated within the cell body rind region (Fig. 2B, C). Sp11 and Sp13 were located outside the brain (Fig. S5D), could not be associated to a single-cell cluster and were respectively marked by expression of salivary gland protein 2-like and odorant binding protein 48, and they were therefore respectively assigned to salivary gland and antennae tissue. Two other clusters (Sp3 and Sp6), positioned in the anterior and posterior parts of the head sections, were not associated to any Ag cluster and were marked by expression of multiple myosins (Supplementary Data 3), and were therefore assigned as muscle tissue. Sp9 was consistently situated at the front of the head and was assigned to clypeus (Fig. 2C and Fig. S6). Finally, the eye region was defined by several clusters (Sp0, Sp5, Sp7, Sp8), which corresponded to optic lobe and photoreceptor single-cell clusters and were situated in proximity to spots with expression of vision-associated genes, such as arrestin 2 (Fig. 2C and Fig. S5E), a classical retinal protein³⁵.

Fig. 2 — A Schematic of sectioning orientation of mosquito heads. H&E staining of a sagittal and an axial cut of *An. gambiae* female heads; eye (black arrows), muscle (green arrows), antenna (magenta arrow), and proboscis (blue arrow) are annotated (scale bar = 10 µm). B ISH imaging of axial histological sections of a mosquito head (front of the head on the top, scale bar = 100 μm), highlighting nuclei (DAPI) and the neuronal marker *elav-like* (oriented with the front of the head on top and the right side of the head on the right). C Left panel: UMAP representation of 9, 914 high-quality spatially-resolved transcriptomes. The transcriptomes were clustered and grouped by the predicted major cell types (bold italics). For each Sp cluster, notable marker genes (*italics*) and likely tissue of origin (bold) are indicated. Right panel: Sp clusters displayed in their spatial context in five high-quality aligned head regions (oriented with the front of the head on top and the right side of the head on the right). Normalized expression of the neuronal marker *elav-like* and the eye-associated marker *arrestin 2* are displayed.

A spatially-resolved map of cellular diversity in the An. gambiae brain

We next combined our cell atlas and our spatially-resolved data to determine the ontology of different cell populations within the brain, understand marker conservation and diversity, and identify important structures, thereby providing the first comprehensive transcriptomic cartography of the female An. gambiae brain.

Glia

Glia are pivotal for neuronal development and function, and have been shown to be at the root of behavioral responses²¹. The three repo-expressing clusters Ag10, Ag23 and Ag25 were all strongly associated with glial cell types in the reference datasets (Fig. 1A–C and Figs. S3B, S7A). All glia clusters expressed thr3DH (AGAP011948) (Fig. 3A), an ortholog of Dm CG5955, which is involved in sleep regulation in the fly brain, a function strongly associated with glia^36,37. Notably, all glial clusters expressed genes involved in lipid mobilization (Supplementary Data 3), a well-established glial function which fuels neural metabolism: they included obp9, an ortholog of Dm obp44a with a role in lipid trafficking in the astrocytes of the fly³⁸, and lipocln (AGAP005223), an ortholog of Dm glaz, known to work as a glia-neuron lipid shuttle³⁹ (Fig. S7B–D).

Surface glia have a unique role of insulating the brain from the hemolymph forming a blood-brain barrier. SAMap associated Ag25 to glia, perineurial and sub-perineurial glia (Supplementary Data 2), and the cluster expressed surface glia markers: tret1, an ortholog of Dm tret1-1, a marker of perineurial glia in the fly brain⁴⁰, as well as ABCB2 (AGAP005639), an ortholog of Dm mdr65, a marker of sub-perineurial glia⁴¹ (Fig. 3A and Fig. S7D). We used ISH to determine the distribution of tret1-expressing cells. Our probe panel measured RNA signals from elav-like, repo and tret1 transcripts. We identified a dense neuronal cell-body rind using the elav-like signal and observed that repo-expressing cells were broadly distributed, primarily within the soma-rich region. In contrast, tret1 had a continuous distribution on the surface of the cell body rind (Fig. 3B), suggesting that the An. gambiae brain is insulated by surface glia expressing tret1. Interestingly, the Sp1 spatial cluster partially matched Ag25 and was marked by surface glia markers (Supplementary Data 2). Despite an only partial overlap of Sp1 with tret1 expression, the spatial cluster was distributed peripherally within the mosquito head (Fig. 3C), and located at the immediate proximity and at the surface of the cell body rind Sp2 (Figs. S5F, 6). We concluded that Ag25 and Sp1 confirm the presence and distribution of surface glia in the An. gambiae brain. Interestingly, we could not differentiate within Ag25 between perineurial and subperineurial glia, with more than a third of Ag25 cells expressing both tret1 and ABCB2. This suggests a possible divergence in functional sub-compartmentalization between mosquito and fly surface glia.

The ISH distribution of repo confirmed the presence of glial cells within the cell body rind and the neuropil (Fig. 3B). Concordantly, Sp4 was associated with Ag10 and was distributed within both cell body rind and neuropil regions, and mostly mirrored the expression pattern of scmt1 (AGAP001185) (Fig. 3B, C), an ortholog of the neuropil-associated glia marker Dm rumpel⁴². SAMap evidenced a strong similarity of Ag10 to D. melanogaster ensheathing, cortex and astrocyte-like glia, while Ag23 was mapped to astrocyte-like glia (Fig. S3B, Fig. S7, Supplementary Data 2). In addition, both of these single-cell clusters expressed known glial markers (Fig. 1B, Fig. S7B–D, and Supplementary Data 1). However, they did not align with specific markers of glia cell type in D. melanogaster, and we observed co-expression in the same cell of markers known to be exclusive of specific cell types in the fruit fly. For instance, we observe co-expression of orthologs of Dm ebony (ebony) and rumpel (scmt1), respectively markers of astrocyte-like glia⁴³ and ensheathing glia⁴², in 45% of the cells in Ag10 and Ag23, as well as the wide expression of turtle in Ag10, an ortholog of Dm bdl, a marker of wrapping glia⁴⁴. This suggests that functional markers of D. melanogaster neuropil-associated glia are not conserved in An. gambiae. However, we capture a rich transcriptional diversity within Ag10 and Ag23, and identify markers of different cell types (Supplementary Data 1, 3), which may be leveraged to understand unique glia biology in the mosquito brain.

Vision and circadian neurons

The insect visual system occupies a big volume in the brain (Fig. 4A) and consists of well-defined regions (photoreceptors, lamina, medulla, lobula complex), that are composed of distinct cell types^45–47. Our single-cell atlas identified several putative vision-associated cell types with defined marker expression (Fig. S8A,C and Supplementary Data 2). They could be grouped in broad categories, including photoreceptors (Ag15) as well as various optic lobe-associated neurons comprising (i) lamina neurons (Ag11, Ag19 and Ag24), (ii) putative proximal medulla interneurons (Pm) (Ag40), (iii) putative C2/C3 cells (Ag39), and (iv) uncategorized optic lobe neurons (Ag4, Ag12, Ag17, Ag30, Ag34, Ag35, and Ag36). We also determined that the expression of either one of two markers identifies different sub-populations of the optic lobe neurons: adcy8 (Dm rut) (Ag11, Ag19, Ag30, Ag36) and D1-like dopamine R2 (Ag17, Ag24) (Fig. 4B). To validate the annotation and these marker genes, we performed multiplex ISH of these two markers in regions where both the ommatidia and the cell body rind (elav-like positive) were discernible, and showed that both markers were present in the lamina with distinct expression patterns, confirming them as appropriate markers of these cell populations and the annotation as optic lobe cell types (Fig. 4C). Interestingly, our spatial transcriptomic dataset captured the expression pattern of the whole eye from photoreception (Sp0, Sp5, Sp7) to signal integration in the lamina (Sp8) (Fig. 2C), and displayed a highly structured pattern (Fig. 4D). We reasoned that we could use these data to identify genes related to vision and signal integration. We therefore selected three regions where the tissue sectioning captured the full transition from the lens through the ommatidia and to the optic lobe neurons' soma in the cell body rind, and performed an expression-by-distance analysis to identify and contextualize vision-associated genes (Fig. 4D and Fig. S9). Using this approach, we identified 103 candidate genes linked to the eye region (Supplementary Data 4). These ranged from eye-specialized cuticular protein (e.g., cpr132)⁴⁸, peripheral opsins in the photoreceptor cell (e.g., gprop8)⁴⁹, through signal transduction factor (e.g., arr2)³⁵ to signal integration in the lamina (e.g., HisCl AGAP001913, elav-like) (Fig. 4D, E). Opsins are key to photoreception and have been shown to have expanded in different dipterans⁴⁹ but their specific role is unclear in Anopheles⁵⁰. Here, we detected five dominantly expressed opsins (gprop1, 3, 4, 6, 8) in photoreceptors (Ag15) (Supplementary Data 3) and within the ommatidia (Figs. S8D, S9 and Supplementary Data 4). Among those there are four representatives of the Rh6 (long wavelength sensitive gprop1, 3, 4, 6) that are responsive to green light in Drosophila species but have been shown to have undergone diversification in Anopheles⁴⁹, and a single putative Rh3/4 ultraviolet wavelength-sensing opsin.

Vision is intimately linked to circadian rhythms and is usually mediated by specialized interregional neurons with projections spanning multiple neuropils. Interestingly, Ag11 and Ag30 were characterized by AGAP001726, an ortholog of Dm wake, a clock gene expressed in latero-ventral clock neurons (LNvs)⁵¹. Although lamina and LNvs have different tissue localization and roles, lamina neurons have intimate interaction with clock neurons and express clock genes in D. melanogaster^52–55. Circadian clock cell type composition in D. melanogaster includes approximately 150 neurons per brain, organized into lateral neurons and dorsal neurons⁵⁶. Ag37 was strongly associated with D. melanogaster clock neurons (LNvs) in the multispecies dataset panel (Fig. S8A and Supplementary Data 2). In addition, Ag37 was strongly marked by AGAP003739 (Dm fer2) (Fig. S8B and Supplementary Data 1), which is expressed in protocerebral anterior medial/lateral neurons and in LNvs^57,58. The cluster was marked by gprcal3 (Dm pdfr), and a subset of cells in the cluster expressed zic (Dm opa) and pdh (Dm pdf), confirming a clock neuron cell identity^56,59. Furthermore, Ag37 expressed cardioacceleratory peptide (Dm ccap) (Supplementary Data 3), which is involved in the regulation of circadian behaviors^60,61. In our spatial data, Sp12 was annotated as a clock neuron-enriched cluster (Supplementary Data 2). Sp12 was present in only two high-quality head sections, and was positioned in a lateral anterior focus within the cell body rind. The other heads were likely sectioned in a different plane devoid of Sp12 (Fig. 4E), suggesting that the distribution of cellular bodies of clock neurons has a spatially restricted focus in the An. gambiae brain. In agreement with these observations, we noted ISH foci of AGAP003739, one of our clock neuron markers, localized in soma-rich structures, in continuity with those lining the outer optic lobe (Fig. 4F). This location matches the one described for subsets of D. melanogaster clock neurons⁵¹ (Fig. 4A). Altogether, our data showed a cellular population enriched in clock neurons (Fig. S8B), with a focal location within the An. gambiae brain.

Kenyon cells

MBs are paired structures in insect brains, including Anopheles’^15,33 (Fig. S4), involved in olfactory associative learning, memory and goal-directed behavior⁶². The MBs are primarily composed of KCs (Fig. S10A). D. melanogaster (Diptera) KC axons typically form horizontal and vertical α/β, α′/β′, and γ lobes, but can be further functionally subdivided into seven subtypes with a likely unique molecular profile³⁰. A. mellifera (Hymenoptera) instead possesses five major KC types, divided in Class I (sKC, mKC, ℓKC types), and Class II (II-KC1 and II-KC2 types)⁶³. We identified three distinct KC types of the An. gambiae MBs: Ag14, Ag28, and Ag32 were associated with KC clusters in the cross-species comparison (Fig. 1B, C and Fig. S3B) and expressed multiple KC-associated genes and markers (Fig. S10B–E and Supplementary Data 1–3). Ag28 expressed AGAP000585 the ortholog of Dm mamo which specifies a cell’s fate in D. melanogaster α′/β′- and γ-KC^64,65 (Fig. S10B, E and Supplementary Data 2); it showed the greatest similarity to D. melanogaster α′/β′ KCs and Ae. aegypti KCs, and low similarity to A. mellifera KC subtypes (Supplementary Data 2). We further noted that Ag28 and Ag32 were marked by AGAP029446, an ortholog of Dm trio, a marker of D. melanogaster α′/β′ and γ KCs⁶⁶. Ag14, Ag28 and Ag32 were also marked by expression of genes important for MB development such as drn-tre (Dm rn-tre)⁶⁷ and for memory formation in MBs, such as nuclear receptor subfamily 2 group E member 3 (Dm hr51), slingshot (Dm ssh), adcy8 and D1-like dopamine R2 (Dm dop1R2), gprdop1 (Dm dop1R1), and multiple protein kinase A subunits (AGAP029637, AGAP006448 and AGAP004940)^68–74 (Fig. S10C, E and Supplementary Data 3). Although not individually aligned with different KC subtypes in the reference datasets, the aforementioned transcriptional signature suggests conserved KC functions in the An. gambiae brain, including MB neuroblast proliferation (e.g., mamo⁶⁴), axon growth and guidance, and MB neural network wiring (e.g., trio, drn-tre^66,67). We further identified a small subcluster of Sp2 that was characterized by the expression of markers of Ag14, Ag28, and Ag32 (Supplementary Data 3). We isolated these 18 spots (Fig. S10F) and conducted a marker analysis, identifying 11 marker genes,eight of which were shared with KC single-cell clusters (Supplementary Data 5). These spots clustered in a single focus within the brain, which expressed KC markers and therefore corresponds to an An. gambiae MB. Mu and colleagues observed high similarity of hymenopterans (i.e., A. mellifera and M. pharaonis) sKCs and II-KC1 types with dipterans KCs (i.e., D. melanogaster)⁸. In concordance, our analysis showed that KCs of An. gambiae (Diptera) have the highest similarity to A. mellifera sKCs and II-KC1 types; however, we could also observe relatively high similarity of the KC cluster Ag14 to A. mellifera mKCs (Fig. S3 and Supplementary Data 2). Altogether, we defined three distinct KC types of the An. gambiae MBs associated with distinct and An. gambiae-specific transcriptional profiles.

Immune cells

Three types of hemocytes are described in Anopheles: the phagocytic granulocytes, the melanin-producing oenocytoids, and the prohemocytes, possible precursor cells of the two mature hemocyte types⁷⁵. Ag42 was associated with Drosophila plasmatocytes and expressed numerous An. gambiae granulocyte markers, including lrim16a, lrr8, col4a and sparc²⁶ (Fig. S11A–D, Supplementary Data 1). Ag38 specifically expressed scrb9, a univocal marker of An. gambiae oenocytoids among immune cells²⁶ (Fig. S11B and D), and was the only cluster showing a purely dopaminergic profile (Fig. S3A) which is consistent with its immune function⁷⁶. We did not identify prohemocytes, suggesting there is no hematopoietic differentiation in and surrounding the brain. Immune cells were also captured in our spatial data (Sp10), and were distributed throughout the head samples, but showed a preferential proximity to head muscles (Sp3, Sp6) and glia cells-containing foci (Sp1, Sp4) (Fig. 2C and Fig. S5F). We further confirmed granulocyte presence in the An. gambiae female head by ISH, which consistently showed col4a-positive cells associated with elav-like-negative nuclei at the back of the mosquito head (Fig. S11E). Interestingly, the ISH data also suggested that granulocytes can locate between the lamina and the ommatidia (Fig. S11E). Altogether, our results show for the first time mosquito immune cells located in the brain periphery, at the limit of the insect blood-brain barrier, and are consistent with a recruitment of mature hemocytes from the hemolymph compartment^77,78.

Other cell types and structures

Ag0 expressed markers suggestive of a progenitor state (Supplementary Data 1) such as numb, which in D. melanogaster controls asymmetric division in sense organ precursors^79,80 and planar cell polarity establishment in eyes⁸¹, suggesting a possible optic lobe progenitor cell type.

We reasoned that the tight packing of the cell body rind in combination with current technical restrictions of spatial transcriptomics could lead to multiple cell types being captured in the Sp2 cell body rind cluster and render the identification of specific structures difficult, especially those composed of a few neurons in each brain. To establish whether we could recover specialized structures within the cell body rind, we selected two marker genes of cell clusters captured in the atlas, vmac, among the top ten markers of Ag7 (enriched with monoaminergic/peptidergic neurons), and mip, a marker of MIP-neurons expressed in Ag4, which can regulate feeding and mating behaviors^82,83. Robust expression of these markers identified sub-clusters within Sp2 (Fig. S12A, B), which had a discrete localization in the head, suggesting monoaminergic/peptidergic and MIP neurons mark discrete structures within the brain of An. gambiae as in D. melanogaster^83,84.

Life history is associated with distinct brain transcriptional states

The different life phases of adult female anophelines are associated with distinct behaviors such as mating or host-seeking, which are likely supported by distinct neuro-transcriptional states^8,85,86. Typically, a virgin female will mate prior to blood feeding and may become infected by malaria parasites upon a bloodmeal on an infectious human. In the case of An. gambiae infection by P. falciparum, it will then take ~ 10–14 days for sporozoites to colonize the salivary glands and render the mosquito infectious to another human host, so that only older mosquitoes can transmit parasites⁸⁷. We reasoned that these states may vary significantly among individuals, and therefore developed a protocol for single-brain RNA-seq (Fig. S1B). We first compared brains from virgin and mated females. Almost all the 121 differentially expressed genes (DEGs) in whole brains were upregulated after mating (Fig. 5A, Supplementary Data 6), including transcription factors with orthologs involved in circadian rhythm and behavioral regulation, cell fate specification, and axon guidance^88–90, chromatin repressors and neural network remodelers^91,92, suggesting that mating triggers active rewiring and signaling in the female mosquito brain (Fig. 5B). In addition to that, we identified a post-mating upregulation of a putative receptor for serotonin (GPR5HT1A), a neurotransmitter associated with mating refractoriness in D. melanogaster⁹³. We further conducted differential expression analysis (DEA) in our scRNA-seq data, which revealed that neuronal clusters account for most of the transcriptional shift (Fig. 5C and Supplementary Data 7). In particular, mating appears to induce a strong downregulation of cabut (AGAP009889) in Ag37 (clock neurons-enriched), this gene has previously been shown to be involved in circadian clock activity and energy metabolism in response to sugar sensing in D. melanogaster⁹⁰, as well as downregulation of malate dehydrogenase gene (AGAP009510), and of genes involved in signaling, such as guanine nucleotide binding protein subunit (AGAP010318), D1-like dopamine R2, and HisCl AGAP001913. Mating was associated with an upregulation of genes with orthologs involved in cell growth and adhesion, such as AGAP006282 (Dm Fs), AGAP011194 (Dm klg, a cell adhesion molecule) and AGAP029128 (Dm mfas, involved in axonogenesis), as well as obp9 and the aminopeptidase AGAP000129. We also note the upregulation of some genes classically involved in light signal processing, such as arrestin-1 (AGAP010134), GPROP1 (AGAP013149), GPROP3 (AGAP012982), and GPROP4 (AGAP012985) in Ag37.

We then compared the brains of age-matched sugar-fed mated females with those of mated females 24 h after a bloodmeal (Fig. 5D). Single brain differential expression analysis identified 1, 895 DEGs (Fig. 5D and Supplementary Data 6). Blood feeding induced a profound alteration in genes associated with translation activity. This was evidenced by an overall downregulation of genes encoding ribosomal proteins, translation initiation and elongation factors, and tRNA synthetases, suggesting a decrease in translation processes in the brain following blood feeding. This was also associated with an increase in transcription of proteasome proteins further pointing to a reshaping of proteostasis (Fig. 5E and Supplementary Data 6). A concomitant downregulation of genes involved in cell proliferation and intracellular signaling (e.g., putative GPCR receptors, kinases of the MAPK cascade, Ser/Thr kinases, ion channels) could indicate a lowered response to stimuli, consistent with the refractory/resting interval after a bloodmeal⁹⁴ (Supplementary Data 6). On the other hand, we saw increased expression of genes associated with energy metabolism, such as enzymes of the purine, pyrimidine, amino acid biosynthesis, glyoxylate and dicarboxylate metabolism, the Krebs cycle, and oxidative phosphorylation after bloodmeal (Fig. 5E and Supplementary Data 6). Interestingly, we identified an odorant binding protein gene, obp9 (AGAP000278), strongly upregulated following bloodmeal (Fig. 5E and Supplementary Data 6), consistent with previous proteomics studies on whole An. gambiae heads⁹⁵. This gene is a robust marker of neuropil-associated glia (Ag10) in our atlas (Fig. S7 and Supplementary Data 3). The processes triggered by bloodmeal showed consistent trends in bulk and scRNA-seq data (Fig. 5E, F and Supplementary Data 8). The DEA per-cell cluster revealed a drastic glial transcriptional switch after bloodmeal, consistent with what has been observed for Ae. aegypti⁷. The top upregulated genes in glia clusters Ag10 and Ag25 are hemolymph proteins, and unexpectedly showed a similar pattern in KC clusters (Fig. 5E). For instance, obp9 expression was strongly induced by bloodmeal in KCs, together with AGAP007663, an ortholog of multiple 27 kDa hemolymph protein-like genes in Aedes and Anopheles species. Blood feeding also enhanced immunity genes expression in the brain with upregulation of genes coding for (i) peptidoglycan recognition proteins (pgrps1, pgrps3) (ii) the Toll-pathway activator spz1, pro-inflammatory TNF-α receptor ligand eiger⁹⁶ (iii) melanization (CLIP domain serine proteases, srpn2, yellow protein) (Iv) genes coding for antimicrobial peptides (cec1, def1)⁹⁷ and (v) extracellular matrix-reshaping protein coding genes (Supplementary Data 6), which are involved in cell migration^98,99. These genes are predominantly expressed by the hemocyte and the glia clusters in the cell atlas (Fig. S13A), with clipB13 (AGAP004855) being significantly upregulated after bloodmeal in both Ag10 and Ag25 glia clusters (Supplementary Data 8), suggesting an intrinsic brain immune response to blood-feeding.

Behavioral manipulation by parasites is a widespread adaptive strategy and several studies have shown that malaria parasites can alter mosquito feeding behavior in a way that favors their transmission^10–12. We considered that such a manipulation would likely have effectors in the brain of the mosquito. We generated single brain transcriptomes of P. falciparum-infected and uninfected mosquitoes, with a wide range of infection intensities (Fig. S1C and Fig. 6A, B). We compared infected mosquitoes to their non-infected counterparts at (i) 8 days post infection (dpi), when parasites are at the oocyst stage and are not infectious, and (ii) 16 dpi, when parasites are at the infectious sporozoite stage in salivary glands. Surprisingly, our analysis revealed no consistent transcriptional difference at either time point in the brain transcriptional profile, with only def1 being upregulated at 8 dpi when replicates are combined (Fig. 6C, D and Supplementary Data 6). All the infected mosquitoes had the same age when challenged with P. falciparum (i.e., 6 days old), and were dissected with their non-infected controls either 8 or 16 days later, when they were 14 and 22 days old, respectively. We therefore explored the influence of aging on the brain and identified a robust transcriptional difference between day 14 and day 22 brains independent of infection status (Fig. 6D–F, Supplementary Data 6). Interestingly, we observed signals known to be associated with aging in other systems. For instance, insulin-like peptides, associated with age-related nutrient sensing¹⁰⁰, were all upregulated (7/7) in older mosquitoes (Supplementary Data 6). Additionally, immunity impairment with age is well established¹⁰¹ and we evidence an upregulation of CLIP serine proteases (FDR = 2.92E-07) and serpins (FDR = 3.72E-03) (Fig. S13), which have a role in modulating inflammation¹⁰², and are likely induced by immune cells and glia (Fig. S13B, C). However, the top upregulated genes with age were mainly linked to oxidative phosphorylation (OxPhos), genes that are expressed in most cell types (Fig. 6G and Fig. S13D). This result contrasts with a usual decline in OxPhos gene expression with age in insects^103,104. Our observations may reflect an early compensatory mechanism linked to the onset of impaired mitochondrial function in the aging brain¹⁰⁵. Taken together, these data suggest that aging in the female An. gambiae brain is initiated by a dysregulation of both mitochondrial function and immunity.

Fig. 6 — A Prevalence and intensity of infection were scored during sample dissection in female *An. gambiae* mosquitoes 8 days after the infection challenge (14 day-old individuals). B Prevalence and intensity of infection scored during sample dissection in female *An. gambiae* mosquitoes 16 days after the infection challenge (22 day-old individuals). C PCA of single brains transcriptomes from infected or non infected females. D Distance plot showing the number of DEGs found in one-to-one infection condition comparisons (infection status and age) (*ashr* method). E PCA of single brains transcriptomes from 14 or 22-day-old females. F Volcano plot of differentially expressed genes (adjusted p-value < 0.05) between 14 and 22-day-old females; genes having absolute logarithmic fold-change > 1.25 are highlighted (red and blue). G Heatmap of top differentially expressed genes from (F) (|log₂FC | > 1.5 and FDR < 1E-20). Data were obtained from four experimental conditions, per N = 2 experimental replicates per condition, for a total of n = 78 single brain samples. Multiprotein bridge factor 2: MBF2; Oxidative phosphorylation: OxPhos; purine and pyrimidine metabolism: PPM.

Discussion

In this study, we present a survey of cell type composition and tissue gene expression patterns of the brain of the primary malaria vector. We confidently identified broad cell type categories (e.g., glia, KCs) within the An. gambiae brain. For many of them, we could ascribe a more granular identity. In some cases, cross-species homologies were supported by shared ortholog marker genes (e.g., surface glia, monoaminergic/peptidergic neurons), while other cell types possessed a signature specific to the Anopheles brain among the contrasted species (e.g., neuropil-associated glia). For all Anopheles cell types, we also defined marker genes, some of which show species-transcendent patterns while others show species-specificity. This represents a foundational map of the female Anopheles brain, supporting future functional characterization of neuronal populations and further characterization of known and novel cellular mechanisms in insect neurobiology.

We determined the diversity and spatial organization of the major cell populations of the brain, namely glial cell types, optic lobe neurons, immune cells, KCs, clock neurons and other central brain cell types, using high-resolution ISH of selected candidate markers and leveraging univocal assignments of 50% of the spatial transcriptomics spots to Ag clusters, combined with putative marker expression patterns. Our study lays the ground for future studies to hopefully comprehensively characterize gene expression in the Anopheles brain with a high-resolution 3D localization mapping.

A large cell population displays neuroblast-like features (Ag0), suggesting that it is composed of neural progenitors. While neurogenesis has been shown to take place in the brain of some adult insects both in physiological conditions and after injury^106–109, to our knowledge it has not been reported in an adult mosquito brain.

Despite the evolutionary divergence between Anopheles and established model organisms, our data revealed broadly conserved gene expression and spatial organization consistent with patterns previously described in other species. Some clusters appear to be specific to Anopheles (e.g., Ag13), highly similar among mosquitoes (e.g., Ag29, Ag31, Ag43), shared across Diptera (e.g., Ag41), or conserved across orders included in this analysis (e.g., Ag25). Furthermore, we observed that dipteran species share a broadly concordant distribution of KC subtypes, whereas hymenopteran species exhibit KC subtypes (e.g., ℓKCs and II-KC2) that we did not detect in the mosquito samples. This is consistent with the hypothesis that specialized behavioral or sensory functions in Hymenoptera may be supported by lineage-specific cell types, although further sampling and functional validation would be required to confirm this interpretation. Such multi-species cell homology comparison may offer preliminary insight into the functional evolutionary history of insect nervous systems.

Our adult life-stage-resolved single-brain data mapped expression changes linked to transmission-related life history and highlighted that mating induced subtler variations in brain transcription than the massive shift caused by a bloodmeal. Interestingly, we show that mating induced alterations in gene expression associated with neural functions and signaling, including upregulation of light-perceiving genes in clock neurons. The mating-associated upregulation of these transcripts suggests that photic input to the circadian network may be dynamically tuned by reproductive state. Although this is consistent with established direct light sensitivity of deep brain regions and clock neurons in Drosophila and vertebrates^110,111, functional validation will be required to demonstrate active light transduction in An. gambiae clock neurons. As An. gambiae, and many other mosquito species, have a tendency to mate at a specific time of the day (namely, at dusk), this finding opens new questions on whether and how light perception by central brain neurons can drive mating behaviors.

In our data, gene expression 24 h post-bloodmeal is consistent with programmed resting⁹⁴. Switching from sugar to blood digestion corresponds to a massive shift in the amount and type of available metabolic resources. While previous studies identified a general downregulation of metabolism-related genes at the organism level¹¹², our results show a metabolic switch in the brain that contrasts with the systemic responses to bloodmeal, and may support highly energy demanding processes such as nutrient metabolism and memory consolidation^113,114. Our data also advocate for hormonal cross-talk or nutrient exchange guided by glia, and suggest KCs activate similar processes: the strong upregulation of obp9 in both types of cells suggests it might have a non-olfactory role, perhaps associated with lipid transport, as has been hypothesized for the obp9 ortholog in Ae. aegypti¹¹⁵.

Moreover, our data ruled out an extensive transcriptional response to parasite infection in mosquito brains. We can hypothesize that parasitic manipulation did not occur in our parasite-mosquito strains combination and/or lab conditions, knowing that parasitic manipulation in the An. gambiae - P. falciparum couple is detected only in specific contexts^12,116. However, parasitic manipulation could also occur via transcriptionally silent processes or processes that couldn’t be captured by our methodology. The development of a human malaria system in which manipulation is both robust and reproducible is a high priority for characterizing manipulation mechanisms.

Finally, our data provide a set of potential age-grading transcriptional markers of a mosquito’s third week of adult life. This window corresponds to the main life period when mosquitoes contribute to malaria transmission in the field¹¹⁷ and when age-discriminant signals among both field and laboratory reared mosquitoes can be detected by spectrometry methods^118,119. Further exploration could provide relevant targets for the analysis of age structure of field populations.

Our survey of cell type diversity and spatial organization of the brain of the malaria vector provides a foundational effort to understand mechanisms supporting mosquito behavior with the potential of helping identify new strategies to disrupt malaria transmission.

Methods

Mosquito rearing

A colony of An. gambiae (Kisumu strain¹²⁰) was maintained at 27 °C ± 1 °C and 70% ± 20% relative humidity (RH) and photoperiod of 12 h light/12 h dark in the Vectopole insectarium of IRD in Montpellier, France. Larvae were fed with grinded TetraMin (Tetra) powder and adult mosquitoes fed on 10% honey in water ad libitum. A subset of the colony was transferred to Radboudumc (Nijmegen, the Netherlands) for parasite infection, where eggs were collected in plastic mosquito hatching trays in tap water with a drop of Liquifry (Liquifry No1, Interpet). The larvae were fed with TetraMin baby (Tetra). Larvae were maintained at 27 °C ± 1 °C and 80% ± 10% RH and photoperiod of 12 h light/12 h dark and adult mosquitoes at 21 °C ± 1 °C and 80% ± 10% RH and photoperiod of 12 h light/12 h dark, and fed on 5% glucose (D+ Glucose Monohydrate, Sigma 49159) and 0.4% methylparaben (methyl 4-hydroxybenzoate, Sigma H3647-100G) ad libitum.

Single-cell transcriptomics

Mosquito sample conditions

In order to capture the diversity of brain cell types along An. gambiae females, scRNA-seq experiments included individuals at different stages (i.e., virgin, mated, blood-fed) (Fig. S1A). The two biological replicates originated from two separate egg-laying batches. Starting from an initial pool of mosquitoes at emergence, a set of females was placed in a cage without males (virgin condition). The other female and male mosquitoes were kept together in a cage (mated condition), reaching a 40% of prevalence of mating after 4 days, and a ~ 70% prevalence of mated females after 7 days. We thus selected the latter age for sampling, as it best balanced high mating prevalence and a delayed blood meal after mating, a possible stressor. All cages were fed with 10% honey solution ad libitum. On day 6 post-emergence, a subset of the females reared with males were allowed to feed on rabbit blood provided at 37 °C through a glass feeder for 30 min. Engorged mosquitoes were then sorted to constitute the pool of blood-fed females. On the morning of day seven post-emergence, brains of 10 females per condition (virgin, mated, blood-fed) for each replicate were collected. The mosquitoes were anesthetized on ice, and brains were dissected in Dulbecco’s phosphate-buffered saline (DPBS) (Fisher 10217152) on glass slides under a stereomicroscope using steel Dumont #5 dissection forceps. The pharyngeal pump was not removed to avoid subesophageal ganglion damage. For each mosquito, the spermatheca was checked under an optical microscope for the presence/absence of spermatozoids to ensure that the mating state corresponded to the one required for each experimental condition (i.e., virgin = spermatozoids absent; mated/blood-fed = spermatozoids present). Between 8:30 AM and 11 AM, each of three operators dissected brains alternating among mosquitoes belonging to the different conditions. Each experimental condition sample was composed of ten brains, and was then analyzed in a single 10x run.

Single-cell dissociation and scRNAseq protocol

We adapted an existing protocol for insect brain cell dissociation¹⁹. Specifically, pools of 10 dissected brains were placed into 1.5 mL Protein loBind EP tubes (Eppendorf) in 50 μL of ice-cold Sf-900TM III SFM insect cell culture medium (Fisher 12568019). All the tubes in which biological samples were transferred during the protocol had been coated with Bovine serum albumin (BSA)-(Sigma-Aldrich A60003-25G), by incubating them in 1% BSA in sterile Milli-Q water at room temperature (RT) for 30 min, and subsequently emptied and dried under a biological safety cabinet for 30 min. Pools of brains were placed in 125 μL of DPBS (Fisher 10217152) containing 60 mg/mL of collagenase I (Fisher 11500536) and 1.2 mg/mL of dispase I (Sigma D4818-2MG). Samples were kept at 27 °C shaking at 1000 x g for 1 h, every 10 min the samples were pipetted gently ~ 150 times. Dissociation reactions were stopped by addition of 1 mL of DPBS containing 1% of UltraPure BSA (Invitrogen AM2616). The cell suspensions were filtered through a 40 μm mesh-size cell strainer (PluriStrainer 43-50040-51) into a polypropylene falcon tube. Each sample’s cell concentration was measured by staining the cells with an equal volume of Vybrant DyeCycle Green (Fisher 10134622), diluted 1:1000 in DPBS containing 1% UltraPure BSA. Cells were counted in a MilliCell hemocytometer (Sigma MDH-2N1-50PK) with a Zeiss Axio Imager 2 microscope, and inspected with DIC illumination on a glass slide for shape and integrity.

Cells were then pelleted by centrifugation at 800 x g for 5 min at 4 °C. Assuming a loss of ~ 50% of the counted cells, an adequate volume of chilled DPBS with 1% BSA was added to obtain a concentration of ~ 1300 cells/μL. The concentration of each sample was measured and recorded again(between 800 cells/ μL and 1300 cells/ μL). Single-cell suspensions were loaded on a 10x chip with a target recovery of 10,000 cells per sample. Transcriptomes were obtained with a 10x Genomics GemCode Single-Cell Instrument and a Single Cell 3’ Library & Gel Bead Kit v3 (10x Genomics) according to manufacturers’ recommendations (Single Cell 3′ reagent kits v3.1). Amplified cDNA was controlled with High Sensitivity dsDNA quantitation kit (Life technologies Q32851). Libraries were controlled with a Bioanalyzer 2100 (Agilent) and uniquely dual-indexed. Sequencing was multiplexed on a NovaSeq 6000 with paired-end (PE) sequencing (26 and 98 bp) and yielded ~ 1 billion reads per sample.

Spatial transcriptomics

Seven day old An. gambiae virgin females were anesthetized on ice, and whole heads from four individuals per inclusion block (complete with mouthparts, antennae and part of the neck to avoid damaging internal structures) were collected always between 9 AM and 11 AM and aligned with Dumont #5 dissection forceps, then included in 5 × 5 mm plastic molds in optimal cutting temperature compound (OCT) (TFM-C). OCT was solidified by first holding the mold on the surface of isopentane cooled in dry ice, then lowering the whole sample in isopentane for 1 to 2 min. Two inclusion blocks were obtained with this procedure. Samples were stored for at least 12 h at −70 °C before slicing. They were cut axially in a cryostat at −18 °C to obtain 10 μm thick tissue slices, which were collected on glass slides that were stored at RT. Consecutive slices were alternatively used for spatial transcriptomics or stained with hematoxylin and eosin (H&E) to confirm the orientation of the heads and the localization of the anatomical structures. For staining, histological samples were plunged for 1 minute in methanol, followed by 1 min in isopropanol. Glass slides are then laid horizontally and covered for 2 min with hematoxylin, rinsed in tap water and covered for 30 s with eosin. Subsequently, the samples were rinsed again in tap water and dried on a heater at 50 °C. Spatial transcriptomics slices were placed on a Curio Seeker 3 × 3 mm tile (Curio Bioscience SK004), then covered with a 30 µm section of CryoCube provided by the supplier. The tiles were then processed according to the manufacturer’s protocol. Briefly, the tiles were removed from the tile holder and placed into microtubes, and mRNA was captured and hybridized. Reverse transcription was performed in situ, then the tissue was cleared, and indexed beads were dissociated from the tile and resuspended. The second strand synthesis and cDNA amplification were then performed. Libraries were prepared using the Curio bioscience protocol and were individually indexed with the Curio seeker Dual Indexing Primer kit v1 (Curio bioscience K006). Amplified cDNA and library quality was assessed with High Sensitivity dsDNA kit on a Bioanalyzer 2100 (Agilent) and a Qubit (Thermo Fisher). Libraries were pooled and sequenced on a NovaSeq 6000 S4 flow cell using PE150, yielding ~ 650 million reads per library.

Single brain transcriptomics

Brains of seven-day-old female mosquitoes that were either virgin (n = 9), mated (n = 10), or blood-fed and mated (n = 10) (as described above for scRNA-seq samples) were dissected (Fig. S1B). For P. falciparum infections, NF54 asexual and sexual blood stages were cultured in a semi-automated culture system^121,122. Six days post-emergence, An.Anopheles gambiae females were offered either an infectious bloodmeal or a control, uninfected bloodmeal. Mosquito pots were placed under glass midi-feeders¹²³ attached to a circulating water bath set at 39 °C, and allowed to feed in the dark for 10 min. The blood was collected in lithium-heparin tubes (BD 367526), obtained from the national blood bank (Sanquin Nijmegen, the Netherlands). The unfed and the partially fed mosquitoes were removed with a suction device, fully-fed mosquitoes were subsequently maintained at 26 °C ± 1 °C with 80 ± 10% RH and a 12 h/12 h light-dark photoperiod. An egg-laying bowl was placed in the cage and soaked pads with 5% non-sterile glucose solution were placed on top of the cages and changed regularly. Females that were kept until day 22 received a second uninfected bloodmeal 8 days post-infection (dpi) to accelerate oocyst and sporozoite development. Infected mosquitoes were obtained from three different infectious feeding assays, ~ 10 brains (range 8–10) were collected for each timepoint and for each of two biological replicates (Fig. S1C). During brain sample collection, we confirmed the infection status at the level of individual mosquitoes by checking for the presence of (i) oocysts in the midguts at 8 dpi, and (ii) sporozoites in salivary glands of individuals at 16 dpi. The midgut of individuals at 16 dpi were also dissected and intact and ruptured oocysts were counted. Dissected midguts were stained with 0.5% mercurochrome, and oocyst load was assessed by light microscopy. Salivary glands were dissected, ruptured in PBS, and sporozoites were observed by phase contrast microscopy.

Dissected single brains were isolated in 5 μL Sf-900TM III SFM medium (Fisher 11570446) in a well of a 96-well plate kept on ice. RNA was extracted by adding 60 μL volume of a 1.2X ice-cold lysis buffer (120 mM Tris-HCl pH 7.5, 600 mM LiCl, 12 mM EDTA pH 8, 3.6% w/v lithium dodecyl sulfate, 6 mM DTT in nuclease-free water (NFW)) to each well. The samples were left at RT for 1 min under a biological cabinet and subsequently mixed by pipetting up and down ~ 60 times, before storage at −70 °C for at least one night. Pull-down of mRNA in solution was performed with oligo-dT(25) Dynabeads (Invitrogen 61002). Dynabeads were equilibrated in a lysis buffer and 20 µl of beads were added to each well, each well was mixed thoroughly by pipetting and incubated for 15 min at RT while shaking at 1100 rpm. Using a magnet, bead-bound mRNA samples were subsequently washed with two different buffers: first with 10 mM Tris-HCl pH 7.5, 150 mM LiCl, 1 mM EDTA pH 8, LiDS 0.1% w/v in NFW; secondly with 10 mM Tris-HCl pH 7.5, 150 mM LiCl, 1 mM EDTA pH 8 in NFW. The wash buffer was removed and mRNA was eluted by incubation with 10 mM Tris-HCl pH 8 in NFW for 2 min. mRNA was reverse transcribed with an oligo dT30VN (5’-AAGCAGTGGTATCAACGCAGAGTACT₃₀VN-3’; Integrated DNA Technologies) priming and a template-switching mechanism, using a SmartScribe kit (Takara 639538), with 5X first-strand buffer and 9.2 U/μL of SmartScribe reverse transcriptase, 2.8 μM LNA-oligonucleotide (5′-AGCAGTGGTATCAACGCAGAGTACATrGrG+G-3′; Qiagen), 0.9 U/μL of Superase Inhibitor (Fisher 10085994), 9 μM DTT (Fisher P2325), 1 M betaine (Sigma B0300-1VL), and 0.01 M MgCl₂. RT-PCR was carried on with the following program: a 90-minute step at 42 °C, then ten cycles of 2 min at 50 °C and 2 min at 42 °C, with a final step at 72 °C for 15 min. cDNA was then amplified using a KAPA HiFi HS mix (Merck 3736549) and a primer (5’-AAGCAGTGGTATCAACGCAGAGT-3’) with a PCR program with an initial incubation step at 98 °C for 3 min, followed 14 PCR cycles (98 °C for 20 s, 67 °C for 15 s, 72 °C for 6 min) and a final step at 72 °C for 15 min. After a first purification with Agencourt Ampure XP beads (0.8:1), a low-input Nextera protocol including a step for sample dual indexing was used to index every single brain with a specific pair of primers of the Nextera DNA Unique Dual Indexes Set A and C (Illumina 20027213, 20027215). The libraries were again purified with Agencourt Ampure XP beads (0.8:1) and their quality was assessed with High Sensitivity dsDNA kit on a Bioanalyzer 2100 (Agilent) and a Qubit (Thermo Fisher). The libraries were pooled and sequenced on an Illumina HiSeq 4000 with PE150 with ~ 1 million reads per library.

Confocal microscopy

Female An. gambiae were placed in 70% ethanol for five seconds before brain dissection in ice-cold Schneider’s Insect Medium (S2). Brains were fixed in 2% paraformaldehyde solution in S2 at room temperature for 60 min. Fixed brains were then washed 10 min in PBS containing 0.5% Triton-X (PBST), this step was repeated three times. Brains were then incubated in a blocking solution (5% goat serum (Jackson Immunoresearch) in PBST) for 90 min at room temperature. Brains were then incubated with a primary antibody, α-Bruchpilot, nc82, (1:30, mouse, DSHB) in PBST containing 5% goat serum for 72 h at 4 ˚C. Brains were washed for 10 min four times in PBST, and subsequently incubated with a secondary antibody, (Alexa Fluor 547-conjugated goat α-mouse; 1:400; Jackson Immunoresearch) overnight at 4˚C. After four 10 min washes in PBS, brains were mounted in Vectashield (Vector Labs) on a glass slide. An. gambiae brains were imaged with a Leica SP8-UV confocal microscope. Image resolution was 1024 × 512 with 0.5 µm step size and a frame average of 3 with a 20x objective. All images were analyzed using Fiji¹²⁴.

RNA fluorescence in situ hybridization

Sections of whole heads from seven days old An. gambiae of virgin females were prepared as above, by inclusion in OCT (TBS) and collection of 10 µm tissue slices. Mosquito heads were included in OCT in groups of five per mold, to ensure enough material was available for sample preparation. Seventy-six glass slides with four to six block slices were prepared from seven different inclusion blocks, and twenty-four glass slides obtained from five different inclusion blocks were used for ISH preliminary tests and main experiments. Glass slides pre-treatment and probe hybridization were carried out according to the ACD manufacturer protocol (UM 323100 Rev B). Samples were fixed for 30 min in formaldehyde 4% and subsequently dehydrated with increasing ethanol concentrations. Samples were permeabilized with RNAscope protease IV for 30 min. Staining was performed with an RNAscope Multiplex Fluorescent Reagent Kit v2. Hybridized probes consisted of different combinations of three of these probes: (i) ACD Aga-LOC1270460-O1 (ACD 1792161-C1) targeting elav-like; (ii) Aga-LOC1274238-O1 (ACD 1792148-C2) targeting repo; (iii) Aga-Tret1-1-O1 (ACD 1792131-C3) targeting tret1; (iv) Aga-Col4a1-O1 (ACD 1792151-C2) targeting col4a; (v) Aga-Fer2 (ACD 1800291-C3) targeting AGAP003739; (vi) Aga-Adcy8 (ACD 1800531-C2) targeting adenylate cyclase 8; and (vii) Aga-LOC1272412 (ACD 1800521-C3), targeting D1-like dopamine R2. C1, C2, and C3 in the probe codes represent three different fluorescence detection channels associated with three different fluorophores. Chosen fluorophores were Opal620 (Akoya FP1495001KT) for C1, Opal540 (Akoya FP1494001KT) for C2, and Opal650 (Akoya FP1496001KT) for C3. Opal fluorophores were reconstituted in DMSO according to manufacturer’s guidelines and diluted at a ratio of 1:1,500 in TSA buffer (ACD 322810). Nuclei were stained with DAPI provided in the RNAscope kit. Glass slides were mounted with Prolong Gold Antifade Mountant (Thermo Fisher, P10144) for 30 min at RT. Cryo-slices were kept at −80 °C or on dry ice until use. Presence of non-specific fluorescence was checked with an RNAscope negative control probe (ACD 320871), fluorophores alone, and with single probe labeling. Non-specific fluorescence associated with the presence of opal fluorophores was detected in association with ommatidia and keratin in all negative control conditions and was therefore ignored during the interpretation of the results. Imaging was performed on a Zeiss LSM 780 confocal microscope, with X10 (EC PlnN 10X/0.3 DIC I) and X63 (Pln Apo 63X/1.4 oil DIC III) objectives, and ZEN 3.11 software. Fluorophores assigned to the different channels were excited with lasers of wavelength 405 nm (30 mW) (DAPI); 512 nm (25 mW) (C2); 561 nm (20 mW) (C1); 633 nm (5 mW) (C3). The context of the whole head slice was captured for each image, and insets were subsequently captured at higher resolution. The images shown here are representative of the results obtained from each probe set and chosen among 115 relevant images. All images were analyzed using Fiji¹²⁴.

Bioinformatic data processing and analysis

Single cell transcriptomics

Data analysis was carried out with R (v4.2.2)¹²⁵ and RStudio environment (v2023.06.1)¹²⁶. Fastq files were demultiplexed and mapped to the An. gambiae PEST genome (AgamP4.14) with CellRanger (v7.0.0)¹²⁷ and with an annotation file obtained from VectorBase (v57). Low quality transcriptomes were excluded if they had fewer than 1000 unique transcripts, less than 800 genes and more than 20% mitochondrial genes; genes were filtered out if they were present in less than five cells. Background noise from ambient RNA molecules and random barcode swapping was removed with CellBender¹²⁸ (v3.0). Single-cell analysis was conducted with Seurat (v5.0.1)¹²⁹. Doublets were identified and removed with DoubletFinder¹³⁰ (v2.0.4). Transcriptomes were normalized and integrated using SCTransform. Clustering analysis was performed in Seurat kNN method based on Jaccard’s index with a resolution of 0.7 based on 40 principal components and on a clustree (v 0.5.1) branching, until cluster assignments became unstable across resolutions, to avoid overclustering. Positive cluster marker genes were identified by comparing all the clusters against each other (Seurat FindAllMarkers function with a MAST test¹³¹ and a base-2 logarithmic fold change (LFC) threshold of 0.25 and a minimum fraction of 25% of cells in either of the two populations expressing the gene). The same approach was used to assess DEGs among conditions for each cluster. Any significance value inferior to RStudio minimal representable floating number (<2.225074E-308) resulted as zeros in the analysis outputs. Cell type mapping was conducted with SAMap (v1.0.15)¹³² against annotated references of D. melanogaster^19–21, Ae. aegypti⁷, A. mellifera⁸ and M. paraonis⁵. For the analysis, the chosen reference datasets were subset to keep only suitable sample types for an unbiased cell-type assignation as follows: only female flies were kept from the scRNA-seq data from Davie et al.¹⁹, sated flies data were kept from Park et al.²¹, only adult data n. 1 to n. 8 were kept from Özel et al.²⁰, and we downsampled the number of cells for each cell type in the dataset from Li et al.⁵ (50 %). We did not select for specific adult life cycle stages (e.g., based on D. melanogaster age or on the choice between workers or queens in social insects) because we surmised that they could not introduce biases in our analysis. The coding sequences used were of An. gambiae obtained from VectorBase (v57), of Ae. aegypti obtained from VectorBase (v58) of D. melanogaster obtained from FlyBase (r6.55)¹³³, of A. mellifera obtained from NCBI (based on genome assembly Amel_HAv3.1), of M. pharaonis (based on the genome assembly M.pharaonis_V2.0 and downloaded from the Li and colleagues FigShare repository¹³⁴), trying to match the ones used to annotate the reference sc/snRNA-seq datasets. The reciprocal BLAST results were processed to keep only gene IDs as identifiers to match the reference datasets. SAMap pipeline was performed as recommended⁶. We empirically set a threshold of 0.4 of SAMap alignment score for manual cell type assignments to Ag clusters.

Gene orthology was based on orthogroups obtained on OrthoMCL¹³⁵ release 6.21 (7 May 2024) from D. melanogaster whole transcriptome and the list of An. gambiae genes expressed in our data.

Spatial cell transcriptomics

Demultiplexed fastq files of each bloc were processed using Curio Seeker Pipeline (v2.0.0) within the Nextflow environment (v24.10.3, build 5933). STAR (c2.6.1 d) mapping against the An. gambiae PEST genome (AgamP4.14) was conducted by following the pipeline instructions for custom reference genomes using the generate_seeker_reference.sh and pygtftk (v1.6.2) to add missing “gene_names” to the GTF file obtained from VectorBase (v56). Downstream analysis of the data was carried out with RStudio environment (v2024.12.1) using R (v4.4.3). Data were cleaned from unused spots (selecting only spots > 100 UMIs, > 50 genes, <40% mitochondrial mapping reads), combined, and analyzed using Seurat¹²⁹ (v5.3.0). Normalization and transformation were performed with SCTransform (v0.4.1) regressing out bloc and mitochondrial mapping rates. UMAP dimension reduction was performed on 40 principal components, and spots were clustered with the Seurat kNN method based on Jaccard’s index with a resolution of 0.5, based on a 50-dimensional PCA. Cluster marker genes were identified by comparing all the clusters against each other (Seurat FindAllMarkers function with a MAST test¹³¹ and a LFC threshold = 0.5 and a minimum fraction of 25% of spots in either of the two populations expressing the gene). Annotation of spots to mushroom bodies, MIP cells and monoaminergic/peptidergic neurons was performed by combining marker expression and reclustering of spots assigned to spatial cluster Sp2. Differentially expressed genes were obtained from the renormalized data of all spots of this cluster by contrasting assigned spots to all other spots within this cluster using the MAST algorithm as implemented in Seurat using the same parameters as before. Functional enrichment analysis of marker genes was performed using richr (v0.0.37). Deconvolution of spots against the single-cell dataset was performed using spacexr (v2.2.1). Assignment of spots to single brains was performed visually using the fhs function of GatePoints (v0.1.5), and spot coordinates for each brain were rotated around the center using the rotator function of ArtPack (v0.1.0) for representation. Five brain slices were selected for further analysis based on their integrity. Cluster distance analysis of these 5 slices was performed on non-rotated coordinates for every single brain separately by calculating the mean minimal Euclidean distance of each cluster to all other clusters. Non-directional distances were then calculated by taking the mean of each combination. Expression by distance analysis within eye regions was performed by calculating the Euclidean distance of each spot within the selected area to the selected point of origin. Gene expression by distance was modeled by fitting a natural cubic spline (splines package, 10 degrees of freedom) per eye area for all genes in the dataset. To identify genes showing specific spatial expression patterns they were subsequently filtered for a minimum expression range of 1.5 normalized UMIs and a R² > 0.1 of the natural cubic spline fit. Cluster abundances within the eye section were estimated by the frequency of assigned spots to each cluster within a given distance bin of 10 pixels. Distributions were modeled by applying a natural cubic spline (10 degrees of freedom) fit over the binned occurrences of clusters.

Single-brain transcriptomics

Single brain bulk RNA-seq data were aligned to the An. gambiae PEST genome (AgamP4.14) with hisat2¹³⁶ (v2.2.1) and read count performed with HTSeq¹³⁷ (v2.0) with an annotation file obtained from VectorBase (v56). Downstream analysis of the data was carried out with RStudio environment (v2023.06.1) using R (v4.2.2). For life stage analysis, one virgin condition sample was filtered out as it had a read count that indicated a failed amplification (21,961 reads vs 12,748,569 reads on average in other samples). Data from all samples were pooled, normalized and fit to a linear model, and dispersion was estimated with the DESeq function of DESeq2¹³⁸ (v1.36.0). Differential expression analysis (DEA) was carried out using LFC values shrinkage with the apeglm method¹³⁹. Changes in gene expression were considered significant if associated with a Benjamini-Hochberg (FDR) value < 0.05 and an LFC > 0.25.

For infection data, one uninfected sample at day 16 was removed as outlier (9,055 reads vs 5,970,229 reads on average in other samples). For the infection data, the DEA was conducted with different permutations, for instance, with DESeq objects containing: (i) individual uninfected vs infected replicate, (ii) individual day 14 vs day 22 replicate, (iii) pooled samples by either infection status or age. The reference state was set for either infected samples or day 14 samples, depending on the test performed.

For all comparisons, data were normalized and fit to a linear model, and dispersion was estimated with the DESeq function of DESeq2¹³⁸ (v1.36.0). DEA was carried out using LFC shrinkage both with the apeglm method¹³⁹ and with the ashr method¹⁴⁰ especially for more complex designs (i.e., combining infection and age), the method used is specified in the figure legend. Results of the two analyses were contrasted to both obtain highly reliable LFC estimates (apeglm) and to avoid model over-parameterization (ashr). Reasoning that infection could have subtle effects on brain transcriptomes, and that applying DESeq2 shrinkage methods corrects for low-count value dispersion artifacts, the LFC threshold was set to zero, and results having an FDR value < 0.05 were considered significant.

Gene function information was retrieved from gene annotation present in VectorBase database¹⁴¹. Immune gene family enrichment in aging was performed based on categorized gene families in the An. gambiae PEST genome annotation GCA_000005575.2, AgamP4.14 (VectorBase Release 68 in May 2025)¹⁴². To ensure only annotated members were counted, the genes belonging to each immune family were counted based on gene names, and not on gene product description, which could have been automatically assigned without manual curation. The genes considered to be DE for the analysis had a |log₂FC | > 0.25. Hypergeometric p-values were calculated using the upper-tail (right-tail) probability P(X ≥ a). The FDR was adjusted across all known immune families present in the An. gambiae PEST genome using the Benjamini-Hochberg method.

Statistics and reproducibility

H&E stainings on histological slices were repeated tens of times with similar results during both preliminary tests and main experiments. ISH stainings for each probe set was evaluated during both preliminary tests and main experiments over at least 20 different head slices each coming from a different mosquito head.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information^{(19.9MB, pdf)}

Peer Review file^{(3.6MB, pdf)}

41467_2026_76839_MOESM3_ESM.pdf^{(176.4KB, pdf)}

Description of Additional Supplementary Files

Supplementary Data 1^{(56.9KB, xlsx)}

Supplementary Data 2^{(161KB, xlsx)}

Supplementary Data 3^{(2.1MB, xlsx)}

Supplementary Data 4^{(23.9KB, xlsx)}

Supplementary Data 5^{(148KB, xlsx)}

Supplementary Data 6^{(723KB, xlsx)}

Supplementary Data 7^{(20.2MB, xlsx)}

Supplementary Data 8^{(20.2MB, xlsx)}

Reporting Summary^{(92.3KB, pdf)}

Source data

Source Data^{(21.1MB, xlsx)}

Acknowledgements

We are very grateful to Carole Ginibre and the Vectopôle team for providing mosquitoes for these experiments. We would like to thank Marga van de Vegte-Bolmer and Rianne Stoter for generating the P. falciparum parasites and the mosquito infections. We would also like to thank Jolanda Klaassen, Laura Pelser-Posthumus and Astrid Pouwelsen for breeding mosquitoes for the infection experiments. We acknowledge the support of the MRI core cytometry and imaging facility and the MGX sequencing core facility. We are grateful to Sarrah Loulizi and the HistoPathology platform at Institut Pasteur (Paris) for her help in tissue slicing for the ISH experiments. We are also thankful to Audrey Salles and to the UTechS Photonic BioImaging at Institut Pasteur (Paris) for support in confocal imaging. We are thankful to Julien Marcetteau for producing illustrative material. We thank Adam Dobson and Ana Gomes for critical reading of the manuscript.

Author contributions

A.C. and A.M.T. contributed equally. Conceptualization: A.C. and A.M.T. Data Curation: I.Z.R., C.C., and J.W.B. Formal Analysis and Methodology: I.Z.R., C.C., J.W.B., S.O., Q.J.D., A.V., G.J.G., C.A., W.G., R.B., A.R.S., E.P., D.C., A.C., and A.M.T. Investigation: I.Z.R., J.W.B., A.C., and A.M.T. Visualization: I.Z.R., C.C., E.P., J.W.B., and A.M.T. Funding acquisition: T.B., S.M., A.C., and A.M.T. Project administration: A.C. and A.M.T. Supervision: D.C., R.B., T.B., S.M., A.C., and A.M.T. Writing – original draft: I.Z.R., A.C., and A.M.T. Writing – review and editing: All authors.

Peer review

Peer review information

Nature Communications thanks Hao Zheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Funding

This work was supported by a grant from the Parafrap network of excellence awarded to AC (ANR-11-LABX-0024) which supports IZR. This work was also funded by the RIVOC program from Région Occitanie awarded to AC and AMT (201555PIRIPPLERV06) and by Fondation pour la Recherche Médicale PhD fellowship awarded to IZR (FRM-FDT202404018268) and to SO (FRM-ECO202406019154). This work was supported by the French National Research Institute for Sustainable Development (IRD).

Data availability

All raw sequencing data in this study have been deposited in the European Nucleotide Archive at European Molecular Biology Laboratory European Bioinformatics Institute (www.ebi.ac.uk/ena/) under accession number ERP173390. Additional data including source data is accessible on Zenodo (https://doi.org/10.5281/zenodo.21492585). All analysis code is available via Github (https://github.com/Parasitedevbio/Agambiae_Brain_Atlas.git - doi: 10.5281/zenodo.21487293). Source data are provided as a Source Data file. The datasets presented in the study can also be visualized and explored on https://vectorbase.org/ and https://cellxgene.cziscience.com/ associated with the publication reference. Source data are provided with this paper.

Code availability

Analysis code is accessible via Github (https://github.com/Parasitedevbio/Agambiae_Brain_Atlas.git).

Competing interests

Authors declare that they have no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Cécile Cassan, Johannes W. Bagnoli.

Contributor Information

Irene Z. Rossi, Email: irene.rossi@ird.fr

Anna Cohuet, Email: anna.cohuet@ird.fr.

Arthur M. Talman, Email: arthur.talman@ird.fr

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41467-026-76839-z.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information^{(19.9MB, pdf)}

Peer Review file^{(3.6MB, pdf)}

41467_2026_76839_MOESM3_ESM.pdf^{(176.4KB, pdf)}

Description of Additional Supplementary Files

Supplementary Data 1^{(56.9KB, xlsx)}

Supplementary Data 2^{(161KB, xlsx)}

Supplementary Data 3^{(2.1MB, xlsx)}

Supplementary Data 4^{(23.9KB, xlsx)}

Supplementary Data 5^{(148KB, xlsx)}

Supplementary Data 6^{(723KB, xlsx)}

Supplementary Data 7^{(20.2MB, xlsx)}

Supplementary Data 8^{(20.2MB, xlsx)}

Reporting Summary^{(92.3KB, pdf)}

Source Data^{(21.1MB, xlsx)}

Data Availability Statement

Analysis code is accessible via Github (https://github.com/Parasitedevbio/Agambiae_Brain_Atlas.git).

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PERMALINK

The brain of the female Anopheles gambiae mosquito at spatial and single-cell resolution

Irene Z Rossi

Cécile Cassan

Johannes W Bagnoli

Sümeyye Özhan

Adeline Valente

Quentin J Delorme

Geert-Jan van Gemert

Chiara Andolina

Wouter Graumans

Richárd Bártfai

Avinash R Shenoy

David Carrasco

Emmanuel Perisse

Teun Bousema

Sarah H Merkling

Anna Cohuet

Arthur M Talman

Abstract

Introduction

Results

A cellular atlas of the female An. gambiae brain

Fig. 1. A cellular atlas of the female An. gambiae mosquito brain.

Spatial organization of the female An. gambiae head

Fig. 2. Spatial organization of the female An. gambiae head.

A spatially-resolved map of cellular diversity in the An. gambiae brain

Glia

Fig. 3. Diversity of glial cell types within the An. gambiae brain.

Vision and circadian neurons

Fig. 4. Optic lobe and circadian neurons in the visual system of An. gambiae.

Kenyon cells

Immune cells

Other cell types and structures

Life history is associated with distinct brain transcriptional states

Fig. 5. Different biological states in An. gambiae females are associated with distinct brain transcriptional states.

Fig. 6. Impact of Plasmodium falciparum infection on the female An. gambiae brain.

Discussion

Methods

Mosquito rearing

Single-cell transcriptomics

Mosquito sample conditions

Single-cell dissociation and scRNAseq protocol

Spatial transcriptomics

Single brain transcriptomics

Confocal microscopy

RNA fluorescence in situ hybridization

Bioinformatic data processing and analysis

Single cell transcriptomics

Spatial cell transcriptomics

Single-brain transcriptomics

Statistics and reproducibility

Reporting summary

Supplementary information

Source data

Acknowledgements

Author contributions

Peer review

Peer review information

Funding

Data availability

Code availability

Competing interests

Footnotes

Contributor Information

Supplementary information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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