Cell-specific responses of Anopheles gambiae fat body to blood feeding and infection at single-nuclei resolution

Domain: chemistry
Published: 2026-02-24
Editorial outcome: minor_revision

Abstract
The mosquito fat body plays key roles in metabolism and immunity, yet its cellular diversity and specialization remain poorly understood. This study analyzed 97,650 nuclei from the female Anopheles gambiae abdominal body wall at single-nucleus resolution, identifying seven major cell types. Fat body trophocytes are most abundant (~85%), with five subpopulations: basal (T1, T2), metabolically enriched (T3), immune-responsive (T4), and a vitellogenic group (T5) found only in blood-fed females. Sessile hemocytes comprise 7.4% of cells and expression of lipid biosynthesis enzymes increase in oenocytes (1.1%) after immune priming. T4 trophocytes consistently express immune genes, while various cell types respond to bacterial infection. Blood feeding induces extensive transcriptomic changes, notably upregulating vitellogenin and DNA replication genes, indicating trophocyte endoreplication and metabolic shifts. Vitellogenin mRNA was expressed in the first layer of trophocytes facing the hemolymph with apical subcellular localization. This high-resolution atlas reveals specialized trophocyte roles in mosquito immunity and reproduction.

Introduction
Mosquito-borne diseases continue to impose a significant global health burden, with Anopheles gambiae as the primary African vector of Plasmodium falciparum malaria, the most severe form of the disease in humans. Female mosquitoes become infected when they ingest blood from an infected person containing gametocytes. The parasite undergoes a series of developmental stages in the vector, it multiplies, and is transmitted when the mosquito feeds on a new host. Blood feeding is an important physiological event in mosquitoes, essential for reproduction and egg development, and triggers activation of complex hormonal and nutrient-derived signaling pathways that regulate blood digestion in the midgut, yolk protein precursor (YPP) synthesis in the fat body during vitellogenesis, and nutrient uptake by developing oocytes in the ovaries1,2.

The mosquito fat body is a multifunctional organ analogous to both the adipose tissue and liver in mammals. It is the primary site of nutrient storage and energy metabolism3, contributes to the systemic immune response by producing antimicrobial peptides and participating in pathogen melanization4, facilitates detoxification of metabolic waste and xenobiotics5,6, and is a major site of hemolymph protein synthesis3. It also plays a central role in vitellogenesis by synthesizing and secreting yolk proteins into the hemolymph that are transported to the ovaries to support egg maturation1. Structurally, the abdominal fat body consists of flattened lobes loosely attached to the abdominal integument that project towards the visceral organs and are in direct contact with the hemolymph7,8. The integument, the outermost layer of the mosquito body, is formed by an outer layer of cuticle that is synthesized by a single layer of epidermal cells supported by a basal lamina9. The fat body lobes vary in size and are permeated by the respiratory system, which consists of a network of tracheoles9. Based on their anatomical position, the abdominal fat body can be described as ventral, lateral, or dorsal.

The abdominal body wall contains the fat body and other associated cells. Trophocytes are the predominant cell type within the fat body3. These large polyploid cells form the fat body lobes and accumulate nutrient reserves in the cytoplasm in the form of lipid droplets and glycogen granules7,8,10–12. Oenocytes are rounded cells derived from ectodermal tissue, that can be individual or organized in clusters7,13 and contribute to hydrocarbon synthesis for the cuticle14, detoxification15,16, sex pheromone production13,17, and immune priming18. Immune priming in An. gambiae is mediated by the systemic release of a hemocyte differentiation factor (HDF) that consists of a complex formed by lipoxin A4 bound to Evokin, a lipid carrier18–20. This response is triggered when the microbiota comes in contact with the midgut epithelia during Plasmodium ookinete invasion of the midgut20,21. HDF release increases the proportion of circulating granulocytes and enhances the immune response to subsequent infections19–21. Oenocytes express the double peroxidase enzyme (DBLOX), which mediates HDF synthesis, and their numbers increase in primed mosquitoes18.

Other associated fat body cells include pericardial cells, hemocytes, and neurons. Pericardial cells (PCs) are located in the dorsal body wall along the mosquito heart9. They flank each ostium, a strategic site of high flow as hemolymph enters the heart to be pumped out and distributed throughout the mosquito hemocoel9,22,23. PCs are immunocompetent cells that sense pathogens in the hemolymph and are also involved in osmoregulation and detoxification9,22,24,25. Hemocytes are immune cells that circulate in the hemolymph but are also often found associated with the surface of different tissues, including the fat body. For example, periosteal hemocytes aggregate on the heart surface and can phagocytize pathogens present in the hemolymph22. Mosquitoes have a ventral neural cord spanning the thorax and abdomen, as part of the nervous system that coordinates sensory and locomotor signals between the brain and the body9.

Despite the central role of the fat body and its associated tissues in mosquito physiology and immunity, the cellular composition and cell-type-specific responses to blood feeding and infection remain poorly defined. In this work, we used bulk and single-nucleus RNA sequencing (snRNA-seq) to systematically characterize the abdominal fat body of An. gambiae adult females. By integrating molecular profiling with morphological analyses, we identified and classified the major cell types within the fat body and adjacent tissues. Furthermore, we investigated how these cell populations transcriptionally respond to physiological stimuli such as blood feeding, as well as bacterial and Plasmodium infection. Our findings provide a comprehensive cell atlas of the mosquito fat body and uncover dynamic transcriptional programs that underlie its diverse functional roles in metabolism, reproduction, and immunity.

Results
snRNA-seq reveals 12 clusters in the abdominal body wall of An. gambiae
In this study, bulk and snRNA-seq were used to identify both broad transcriptome changes, as well as cell-specific responses of Anopheles gambiae adult female fat body trophocytes to blood-feeding, bacterial challenge and immune priming (Fig. 1A). Bulk RNA-seq was used to capture broad transcriptional changes with high depth coverage, allowing us to quantify global differences in gene expression across different physiological and immune conditions. In contrast, snRNA-seq enabled the resolution of cell-type–specific transcriptional responses and the identification of different trophocytes subpopulations. Using both methods allowed us to distinguish tissue-wide shifts from changes restricted to specific fat body cell types, providing a more comprehensive understanding of how these stimuli reshape fat body biology.

Due to the importance of the fat body tissue on reproduction and immunity, samples were collected from the abdominal wall of healthy An. gambiae females fed on sugar or blood, as well as from females systemically challenged with bacteria or primed by infecting them with Plasmodium berghei (Fig. 1A). Samples from females challenged systemically with a mixture of Escherichia coli and Micrococcus luteus were analyzed 4 h post-injection, while the priming response was evaluated six days after females were fed on P. berghei-infected mice. The response of fat body cells to blood feeding was investigated 24 h after ingestion of a blood meal. The selected conditions represent the major physiological and immune challenges experienced by adult female An. gambiae: nutrient acquisition (sugar feeding), reproductive activation (blood feeding), acute antibacterial defense (systemic bacterial challenge), and long-term immune modulation (Plasmodium-mediated priming). Together, these treatments capture a broad spectrum of stimuli that shape fat body function in nutrition and vector–pathogen interactions.

A robust single-nucleus isolation protocol was established, yielding 700–1200 nuclei per microliter. The 10× Chromium technology was employed to perform 3’ snRNA-seq on pooled nuclei samples. A total of 97,650 high-quality individual fat body nuclei were obtained from all samples combined. More than 2×109 total sequence reads from the fat body nuclei were acquired, with a median of 949 transcripts and 560 genes per nucleus, and a transcriptome mapping ratio ranging from 57.6% to 76.6%. To ensure data quality, cutoffs for the number of counts and features, as well as the percentage of mitochondrial and ribosomal content, were set for each sample (Supplementary Fig. 1). The datasets were integrated after filtering, resulting in 12 unsupervised clusters visualized using a Uniform Manifold Approximation and Projection (UMAP) (Fig. 1B). Specific gene markers for each cluster were identified (Avg log2FC > 0.5, P val adj < 0.05), and candidate markers were selected for validation by in situ hybridization (ISH) (Supplementary data 1, Figs. 1C and 2). One cluster had fewer than 30 cells after integrating all samples (colored in gray in the UMAP, Fig. 1B) and could not be validated through ISH; therefore, it was not considered a relevant cell cluster and will not be further discussed.

Identification and validation of the abdominal body wall cell types
Based on gene markers from each cluster and experimental validation through ISH, we identified eleven clusters corresponding to fat body trophocytes and other cells associated with the abdominal wall. We identified a total of seven different cell types associated with the abdominal body wall of female mosquitoes: fat body trophocytes with five subpopulations (T1, T2, T3, T4, T5), two types of hemocytes (Hm1, Hm2), epidermal cells (EC), oenocytes (Oe), neurons (NC), and pericardial cells (PC) (Figs. 1B, C, 2, Supplementary data 1).

Trophocytes were the most abundant cell type, comprising 85.2% of the cells in the dataset (Supplementary data 2). They are large polymorphic cells with cytoplasm filled with lipid droplets, organized in multiple layers that form the fat body lobes associated with the different segments of the abdominal body wall (Fig. 2A, B). Five distinct trophocyte transcriptional profiles were observed, with clusters T1, T2, T3, and T4 present in sugar-fed samples and cluster T5 present only in blood-fed samples. Galactokinase (GALK - AGAP002914) is a general marker gene expressed in sugar-fed trophocyte clusters (Figs. 1C and 2B). Hemocytes (Hm1, Hm2) are the second most abundant cell type, representing 7.4% of the cells (Supplementary data 2). Some are associated with the hemolymph surface of the fat body, while others permeate the trophocyte layers within the lobes (Fig. 2A, Supplementary Fig. 2). Hemocytes of the granulocyte (Hm1) lineage were the most prevalent (86.9% of hemocytes) (Fig. 2C, Supplementary Fig. 2), identified using Leucine-rich-repeat protein 8 (LRR8 - AGAP004017) as a marker, while Prophenoloxidase 4 (PPO4 - AGAP004981) expression identified oenocytoids (Hm2), representing 13.1% of hemocytes (Supplementary Fig. 2). Epidermal cells (EC) are closely associated with the cuticle (Fig. 2A, D), have small lipid droplets in their cytoplasm, and are organized in stripes (Fig. 2D). They represent 3.26% of the isolated cells (Supplementary data 2), and expression of Echinoid (AGAP010186) confirmed their identity in ISH.

The abdominal wall of adult females has three main regions: ventral, lateral, and dorsal, with significant differences in cell types associated with these cuticular regions (Fig. 2A). Oenocytes (Oe) are present in the ventral and lateral regions, either as individual cells, or organized as cell clusters embedded in the abdominal fat body that lack lipid droplets in their cytoplasm (Fig. 2A, E; Supplementary Fig. 3A, B). They correspond to 1.12% of the cells (Supplementary data 2), with fatty acid synthase (FAS - AGAP001899) as a specific gene marker (Figs. 1C and 2E). Neurons from the neural cord (NC) were observed in the ventral region (Fig. 2A, F), representing 0.84% of the cells, and expression of the resistance to Dieldrin gene (Rdl - AGAP006028), a GABA-gated chloride channel subunit, confirmed their identity by ISH (Figs. 1C and 2F). Finally, pericardial cells (PC) (Fig. 2G) are present in the dorsal region along the heart, representing 0.41% of cells, and express the Clip-domain Serine Protease B14 (CLIPB14 - AGAP010833) as a specific marker (Figs. 1C and 2A). The morphology and transcriptional characterization of the different cell types associated with the abdominal wall revealed a complex organization in the lateral, ventral, and dorsal regions.

Oenocytes are the main cell type involved in immune priming
We performed bulk and snRNA-seq on mosquitoes challenged with P. berghei to assess the transcriptional and cell-specific responses to immune priming in cells associated with the mosquito body wall. Bulk RNA-seq revealed 45 upregulated genes and 1 downregulated gene (logFC > 0.5, FDR < 0.05) in the body wall of primed mosquitoes (Fig. 3A, Supplementary data 3) six days after Plasmodium infection. Notably, 42.2% of upregulated genes were markers of the oenocyte cluster (Fig. 3A, genes with asterisk), with 37.7% previously reported as expressed or enriched in oenocytes14,15. Most of these genes are related to fatty acid metabolism, including propionyl-CoA synthase (AGAP001473), fatty acid synthases (FAS - AGAP001899, AGAP008468, AGAP028049), fatty acid elongases (AGAP005512, AGAP007264, AGAP003195, AGAP003197), fatty acyl-CoA reductases (FAR2 - AGAP004784, AGAP005986, AGAP005984, AGAP005985), stearoyl-CoA desaturases (SCD - AGAP003050, AGAP003051), and cytochrome P450 enzyme (CYP4G17 - AGAP000877).

Fatty acid synthase (FAS, AGAP001899) mRNA expression has strong and specific expression in the oenocyte clusters present in the ventral and lateral abdominal body wall (Fig. 2E, Supplementary Fig. 3A) but is absent in the dorsal region (Supplementary Fig. 3B). We also stained the abdominal wall tissue with a specific probe to detect DBLOX (AGAP008350), an enzyme involved in lipoxin A4 synthesis, confirming the identity of oenocytes as FAS+ and DBLOX+ cells that lack lipid droplets and are individual or organized in clusters (Fig. 3B, Supplementary Fig. 3C–F). Furthermore, we confirmed by ISH that FAS expression induction in response to priming occurs specifically in oenocytes present in the lateral and ventral regions of the abdominal wall (Fig. 3C, D, Supplementary Fig. 3C–F, Supplementary Fig. 4). The induction in expression of FAS observed in bulk RNA-seq was also confirmed by ISH, which showed approximately a two-fold increase of FAS following priming (Supplementary Fig. 4). However, we could not detect a difference in DBLOX expression levels by ISH (Supplementary Fig. 3C, D). Taken together, these observations indicate that oenocytes express many key enzymes involved in lipid biosynthesis and activate their transcription in response to immune priming.

Circulating granulocytes are the final effectors of the immune priming response. To investigate the response of granulocytes associated with fat body tissue, we performed differential gene expression analysis on the granulocyte cluster (Hm1) between naïve and challenged mosquitoes. This analysis revealed 75 upregulated genes and 135 downregulated genes (Avg log2FC > 0.5, P val adj < 0.05) (Supplementary data 4). Some upregulated genes are related to extracellular matrix and adhesion, such as chondroitin sulfate synthase (AGAP001010) and matrix metalloprotease 2 (AGAP011870). Additionally, genes related to cholesterol synthesis were upregulated, including progestin and adipoQ receptor family member 3 (AGAP000144), Patatin-like phospholipase domain containing 5 (AGAP011925), and long-chain acyl-CoA synthetase (AGAP011603) (Supplementary data 4). Among the downregulated genes, 18% are involved in the oxidative phosphorylation pathway, and 10% in carbohydrate metabolism (Supplementary data 4). Although we observed transcriptional differences between granulocytes from naïve and primed females, we did not observe a difference in the proportion of captured nuclei from body wall-associated granulocytes in response to immune priming.

Functional characterization of fat body trophocyte subpopulations
Four trophocyte clusters (T1, T2, T3, and T4) were present in all sugar-fed samples analyzed, while an additional cluster (T5) appeared only in blood-fed females. In general, the transcriptional differences between trophocytes clusters are more subtle and unique markers cannot be used to identify specific subpopulations. T1 and T2 lack unique specific markers (Fig. 1C), whereas clusters T3, T4, and T5 express some markers at higher levels and in a higher proportion of cells than other subpopulations (Fig. 1C, Supplementary data 1). Most markers in cluster T3 are related to protein translation and metabolic pathways such as glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation (Supplementary data 1). In contrast, markers for cluster T4 are associated with immunity, melanization, or endoplasmic reticulum (ER)-related proteins (Supplementary data 1). The expression of different functional classes of marker genes suggests that they represent distinct trophocyte subpopulations that perform specialized functions in the abdominal fat body.

To further characterize transcriptional differences between trophocyte clusters in sugar-fed mosquitoes, we performed ISH using galactokinase (GALK - AGAP002914) as a general trophocyte marker with higher expression in the T1 cluster; inositol oxygenase (MIOX - AGAP003636) as a marker enriched in T3, and lipase (AGAP001652) in the T4 trophocyte cluster (Fig. 4A). Galactokinase was expressed in all trophocytes of sugar-fed females, with heterogeneity in the levels of expression (Fig. 4B, C). Among the GALK+ trophocytes, some also expressed high levels of MIOX (GALK+, MIOXh) (Fig. 4C, D), others expressed GALK and high levels of the lipase (AGAP001652) marker (GALK+, Lipaseh) (Fig. 4C, E); while a few trophocytes had strong expression of both markers (MIOXh, Lipaseh) (Fig. 4F, white arrow). These findings provide direct evidence that although trophocytes have similar morphology there is heterogeneity in the combination of markers they expressed, indicative of some level of specialization.

Immune response of the abdominal body wall to bacterial challenge
The fat body plays an important role in the mosquito systemic immune response by producing antimicrobial peptides and phenoloxidase enzymes that are secreted into the hemolymph and limit bacterial proliferation and mediate pathogen melanization, respectively. However, cell type-specific responses remain poorly understood. The transcriptional response of the abdominal body wall to bacterial infection was explored by challenging adult females with a systemic injection of a mixture of a Gram-negative (E. coli) and a Gram-positive (M. luteus) bacteria, or a sterile PBS control, and harvesting the abdominal body wall 4 h later. This approach ensured the direct delivery of a standardized bacterial load into the hemolymph, triggering a robust and physiologically relevant immune response. The combination of Gram-positive and Gram-negative bacteria provided a broad immune stimulus, ensuring the activation of the major immune signaling pathways.

Bulk RNA-seq analysis revealed 386 upregulated and 232 downregulated genes (logFC > 1, FDR < 0.05) in response to bacterial challenge (Supplementary Fig. 5). We observed a major induction of several antimicrobial peptides such as defensin 1 (DEF1) and cecropins (CEC1, CecB), as well as multiple components of the Toll and IMD pathways, including the transcription factor REL2 and Peptidoglycan recognition proteins (PGRPLB, PGRPLA, PGRPS1) (Supplementary Fig. 5, Supplementary data 5). Bacterial challenge induced expression of many other immunoregulatory genes such as C-type lysozymes (LYSC1, LYSC2, LYSC7), clip-domain serine proteases (CLIPA1, CLIPA2, CLIPA4, CLIPA5, CLIPA12, CLIPA14, CLIPB5, CLIPB6, CLIPB14, CLIPB15, CLIPB17, CLIPB20, CLIPB36, CLIPC7, CLIPE1), Anopheles Plasmodium-responsive leucine-rich repeat proteins (APL1A, APL1B, APL1C), Leucine-rich repeat immune proteins (LRIM1, LRIM4, LRIM20), and thioester-containing proteins (TEP1, TEP2, TEP3, TEP4, TEP6, TEP15) (Supplementary Fig. 5, Supplementary data 5). Most downregulated genes are involved in carbohydrate, amino acid, and fatty acid metabolism, as well as membrane transport (Supplementary Fig. 5, Supplementary data 5).

To identify cell-specific responses to bacterial challenge, we compared the genes with enhanced expression after bacterial challenge in the bulk RNA-seq data with markers highly expressed in each of the cell clusters identified by snRNA-seq. Interestingly, we observed that 44 gene markers of cluster T4 were upregulated in the bulk RNA-seq data after bacterial challenge (Fig. 5A), suggesting that T4 trophocytes mount a strong immune response to infection. To understand the responses of fat body and tissue-associated cells to infection, we performed ISH using probes for DEF1 and TEP1 (Fig. 5B–F). TEP1 is a marker of cluster T4 and is only expressed in a subset of trophocytes in the PBS control (Fig. 5B, left). However, TEP1 is expressed in all trophocytes following a bacterial challenge (Fig. 5B, right). DEF1 is a shared marker of the trophocyte T4 and hemocyte (Hm1 & Hm2) clusters. We were unable to detect DEF1 expression in the control samples (Fig. 5C, left). However, after bacterial challenge, DEF1 is highly expressed in a subset of trophocytes (Fig. 5C, right), as well as in hemocytes (Fig. 5D, lower panel), epidermal cells (Fig. 5E, lower panel), and pericardial cells (Fig. 5F, lower panel).

Differential expression analysis on trophocyte subpopulations revealed a core immune response with 84 genes being upregulated and 54 genes being downregulated in all trophocyte clusters (T1–T4) (Avg log2FC > 1, P val adj < 0.001) (Fig. 5G, H, Supplementary data 6). These genes with broad and strong induction included antimicrobial peptides (DEF1, CEC1, CecB), genes involved in melanization (CLIPB17, CLIPC7, CLIPB15), the complement system (TEP1, APL1C, CLIPA2), and other canonical immune genes, while mitochondrial (ND6, AGAP001502, AGAP007975) and lipid metabolism genes (AGAP000844, AGAP008369, AGAP012352) were downregulated (Supplementary data 6). Interestingly, each trophocyte subpopulation also had specific transcriptional responses to bacterial infection (Fig. 5G, H). As expected, T4 trophocytes had the strongest transcriptional response to infection, inducing expression of 57 genes, many of which are involved in immunity, such as pathogen recognition proteins (GNBPB1, PGRPLA), activation of the Toll pathway (AGAP001798), bacterial cell wall lysis (LYSC2, AGAP001430), microtubule assembly and cytoskeleton reorganization (AGAP012991, AGAP010770, Arf2), protein folding, maturation, and translocation (AGAP010225, AGAP002816, AGAP007361), and intracellular signaling (AGAP029529, AGAP010350, AGAP005352) (Fig. 5G, Supplementary data 6). Downregulated genes in cluster T4 are involved in cholesterol metabolism (AGAP003542, AGAP004845), mitochondrial translation (AGAP009737, mRpL47), transcription (AGAP001106, AGAP029767), and intracellular signaling (AGAP002250, AGAP001110) (Fig. 5H, Supplementary data 6). Most genes in T1 trophocytes were downregulated (29 genes), and 48% of them are involved in oxidative phosphorylation (Fig. 5H, Supplementary data 6). T2 trophocytes induced expression of 10 genes, 7 of which are related to protein folding, protein and vesicle transport, and autophagy (APG8) (Supplementary data 6). Finally, T3 trophocytes increased the expression of 14 genes, some of which are involved in immunity, ER stress, or membrane remodeling (Supplementary data 6). Differential expression analysis of hemocyte, epidermal cells, and pericardial cell clusters revealed upregulation of several core immune response genes that are also induced in the trophocyte clusters (Supplementary data 6).

Response of the fat body trophocytes to blood feeding
The cellular response of trophocytes to blood feeding was investigated by comparing bulk RNA-seq and snRNA-seq of the abdominal fat body between sugar-fed and blood-fed females 24 h post-feeding (PF). In the sugar-fed sample, cluster T1 corresponds to 60.6% of the cells, while in the blood-fed sample (24 h PF), the same cluster represents only 5.1% of the cells. Conversely, cluster T5 represented 0.1% of cells in the sugar-fed sample and becomes 54% of the cells 24 h PF (Fig. 6A, B, Supplementary data 2). These dramatic changes in the numbers of cells in these clusters suggest that cluster T1 undergoes such a drastic transcriptional change in response to blood feeding that the integrational algorithm classifies it as a new cluster (T5), which is only present in blood-fed females. RNA velocity analysis of blood-fed samples indicated enhanced transcriptional dynamics within cluster T5, evidenced by an enrichment of unspliced transcripts and a predominance of positive velocity vectors (Supplementary Fig. 6). In agreement with this hypothesis, cluster T5 is greatly reduced and cluster T1 becomes predominant again 6 days PF (Fig. 6C, Supplementary data 2), when the gonadotrophic cycle has been completed, indicating that the transition of the T1 cluster to T5 is due to a prominent but transient transcriptional response to blood feeding.

To investigate the transition of the T1 cluster to the T5 transcriptional profile, ISH of the abdominal fat body of sugar-fed and blood-fed females collected 24 h and 6 days post-feeding was performed. GALK was used as a general marker of sugar-fed trophocytes, with an enriched expression in the T1 cluster and vitellogenin (Vg – AGAP004203) as a marker of the T5 cluster. In sugar-fed females, GALK is expressed in the cytoplasm of all trophocytes, particularly in the perinuclear region, while Vg expression could not be detected (Fig. 6D, E). In contrast, high levels of Vg expression were detected 24 h PF, while GALK expression levels were low (Fig. 6D, E). Vg is expressed at higher levels in the fat body associated with the lateral cuticle, relative to the ventral and dorsal sides of the tissue (Supplementary Fig. 7). Interestingly, Vg mRNA is only detected in the layer of fat body trophocytes in contact with the hemolymph (Fig. 6E, F). Furthermore, at a subcellular level, Vg mRNA is concentrated on the apical surface of the cell that faces the hemolymph (Fig. 6F). The Vg mRNA subcellular localization was similar in the lateral, ventral, and dorsal fat body (Supplementary Fig. 7). As expected, GALK and Vg expression 6 days PF are similar to those of sugar-fed females (Fig. 6D, E).

Differential gene expression analysis between clusters T1 (sugar-fed) and T5 (24 h PF) revealed that 171 genes were upregulated and 341 downregulated (Avg log2FC > 1, P val adj < 0.05) (Supplementary data 7). During vitellogenesis, the fat body produces yolk protein precursors that are transported to the ovaries. Vitellogenins (AGAP004203, AGAP013109), the main yolk proteins, are highly increased in cluster T5 (Supplementary data 7). Also, proteins involved in breaking down yolk proteins to ensure nutrient availability for the developing oocyte, such as cathepsin B precursors (AGAP004531, AGAP004534), and vitellogenic carboxypeptidase-like protein (AGAP005434) were upregulated in cluster T5 (Supplementary data 7). Lipids are also transported from the fat body to the developing oocytes, and several proteins involved in lipid metabolism and transport, including lipophorin (AGAP001826), the main hemolymph lipid transporter, were induced in cluster T5 (Supplementary data 7). In addition, genes related to amino acid degradation, ribosomal proteins, endoplasmic reticulum (ER) stability, protein transport, and glycosylation were increased (Supplementary data 7). Meanwhile, genes related to autophagy, carbohydrate metabolism, circadian clock, immunity, signaling, and transcription were downregulated (Supplementary data 7).

Bulk RNA-seq analysis confirmed a strong transcriptional response to blood feeding, with 1655 genes upregulated and 1368 genes downregulated (logFC > 1, FDR < 0.01) (Supplementary Fig. 8, Supplementary data 8). In agreement with the differential gene expression of cluster T5, vitellogenin (AGAP004203 had a 5898-fold increase), cathepsin B precursor (AGAP004534, 121-fold increase), and expression of vitellogenic carboxypeptidase proteins (AGAP005434, 2.8-fold increase; AGAP007505, 2.8-fold increase) was also increased 24 h PF (Supplementary Fig. 8, Supplementary data 8). Interestingly, increased expression of genes involved in DNA replication, such as DNA replication licensing factors (MCM3, MCM4, MCM5, MCM6), DNA polymerase subunits (10 different subunits), replication factors (A1, A2, A3, and 3 C-subunits), proliferating cell nuclear antigen (PCNA), ribonuclease H2 subunit B, helicase Dna2, and DNA ligase 1 was observed; as well as several genes related to DNA repair (MSH2, Rad62, RAD50, MRE11, ERCC-1), and cell cycle such as cyclins (A, C, H, T) and cyclin-dependent kinases (CDK1, CDK4) (Supplementary Fig. 8, Supplementary data 8).

To determine which cells associated with the abdominal wall were undergoing DNA replication in response to blood feeding, 5-ethynyl-2’-deoxyuridine (EdU) was injected into the hemolymph of sugar-fed or blood-fed females, 6 h PF. Samples of sugar-fed females and blood-fed females were analyzed 18 h after EdU injection. We were not able to detect EdU incorporation in trophocytes of sugar-fed females (Fig. 6G). In contrast, the majority of trophocytes from blood-fed females incorporated EdU in their DNA, resulting in strong nuclear staining (Fig. 6H). A few EdU+ cells with small nuclei were observed in both sugar-fed and blood-fed mosquitoes (Fig. 6G, H, white arrows). Notably, some of these small-nuclei cells were positive for the hemocyte marker LRR8 (Supplementary Fig. 9). EdU staining was not detected in any other cell types. Staining for phosphohistone H3 (pH3), a marker of mitosis, was negative in trophocytes of sugar-fed and blood-fed females (Fig. 6G, H). Only a few pH3+ hemocytes (Fig. 6H, yellow arrows) were observed in the body wall of blood-fed mosquitoes; all other cells were negative. Blood feeding also triggered a strong downregulation of genes involved in carbohydrate metabolism, such as genes involved in glycolysis, citric acid cycle, beta oxidation, elongation of very long fatty acids, ribosomal biogenesis, mitochondrial organization, protein folding, and the TOLL pathway (Supplementary Fig. 8, Supplementary data 8).

Methods
Ethics statement
Public Health Service Animal Welfare Assurance #A4149-01 guidelines were followed according to the National Institutes of Health Animal (NIH) Office of Animal Care and Use (OACU). These studies were done according to the NIH animal study protocol (ASP) approved by the NIH Animal Care and User Committee (ACUC), with approval ID ASP-LMVR5.

Mosquito rearing
Anopheles gambiae mosquitoes (G3 strain – CDC) were maintained under standard insectary conditions at 28°C, 80% relative humidity, and a 12:12h light:dark cycle. Adult stages were fed ad libitum with cotton pads soaked in 10% Karo syrup. Eggs were hatched in plastic trays containing 1L of deionized water supplemented with a pinch of powdered koi fish food and 10mL of yeast solution (prepared by dissolving two tads of live baker’s yeast in 50mL of deionized water). Larvae stages were reared in trays with 1L of deionized water and fed every other day with fish food pellets (Tetramin, Blacksburg, VA, USA). The colony was maintained by feeding adult female mosquitoes on defibrinated cow blood using an artificial membrane feeding system.

Blood feeding
Female An. gambiae mosquitoes, 4-5 days post-eclosion, were blood-fed on 3-to 4-week-old female BALB/c uninfected mice (Charles River, Wilmington, MA, USA) for 15 min. Prior to feeding, mosquitoes were starved for ~16h. Only fully engorged females were selected and maintained on 10% karo syrup following feeding until sample harvesting. Same-age female mosquitoes maintained on ad libitum 10% karo syrup were used as a sugar-fed control. Abdominal fat body tissues were dissected 24h post-blood meal, along with their age-matched sugar-fed control mosquitoes. Mice were housed in individually ventilated cages at 20–21°C and 56% relative humidity with a 12:12 hour light-dark cycle and provided with sterile water and food ad libitum.

Plasmodium berghei infection
Mosquito infections with Plasmodium berghei were carried out using the transgenic GFP-expressing ANKA GFPcon 259cl2 parasite line46, maintained by serial passages into 3-to 4-week-old female BALB/c mice from frozen stocks. Prior to mosquito feeding, parasitemia was monitored using in vitro exflagellation assays as previously described47. Four-to-five-day-old female mosquitoes were allowed to feed on infected mice once parasitemia reached 3–6% and 1–2 exflagellations per field were observed. Same-age uninfected mice were used to feed control group of blood-fed mosquitoes. After feeding, both control and infected mosquitoes were maintained at 19°C, 80% humidity, and 12:12 hour light:dark cycle. After 48h, mosquitoes were shifted to 28°C, under the same humidity and light conditions, to reduce oocyst development. Female mosquitoes were allowed to lay eggs at 4 days post-blood meal. Abdominal fat body tissues from control and infected samples were dissected at 6 days post-blood meal. A subset of 10–20 mosquitoes from each experiment was used to confirm the infection success by either: (1) oocyst counts at 10 days post-infection, as a direct measurement of infection; or (2) serpin6 expression at 24h post-infection as marker associated with midgut invasion by P. berghei ookinetes.

Bacteria challenge
Escherichia coli and Micrococcus luteus were kept as frozen stocks at −80°C. A pre-inoculum for each bacterium was setup and allowed to grow overnight at 37°C, 250 rpm in a shaker incubator. On the day of the experiment, the pre-inoculum was diluted in fresh LB media and allowed to grow in the same conditions until OD600 of 0.5. E. coli and M. luteus cultures were mixed at a 1:1 ratio and washed with sterile PBS to remove toxins. Adult 4-day-old female mosquitoes were injected with 138 nL of sterile PBS or bacteria mix. Abdominal fat body tissues from control and infected samples were dissected at 4h post-injection. Validation of immune activation of the fat body after bacteria injection was confirmed by qPCR of antimicrobial peptides at 4h post-injection.

Single nuclei isolation
Female mosquitoes were cold-anesthetized on ice and placed on a drop of filter-sterilized adult hemolymph-like saline (HLS) buffer (Calcium chloride 2mM, Potassium chloride 5mM, HEPES pH 7.4 5mM, Magnesium chloride 8.2mM, Sodium chloride 108mM, Sodium bicarbonate 4mM, Sodium phosphate 1mM, sucrose 10mM, Trehalose 5mM – 48). To isolate the abdominal fat body tissue, the abdomens were separated from the thorax. The gut, ovaries, and Malpighian tubules were then removed from the body cavity, leaving the abdominal fat body attached to the cuticle. For each experimental condition, two pools of 25 tissues were dissected and gently squeezed on ice in HLS buffer with a plastic pestle. The samples were washed twice with 80% methanol to fix and remove lipids. Centrifugations were carried out for 5 min at 500 × g at 4°C. To release the nuclei, 500 µL of lysis buffer (1 mL Nuclei PURE lysis buffer, 1 ul 1M DTT, and 10 uL Triton X-100; Sigma, MO, USA) was added to each sample and homogenized using a pre-cooled 1 mL Dounce tissue grinder, applying 15 strokes with both the loose and tight pestle. Samples were gently mixed with 1.8 mL of a 1.8 M sucrose cushion solution (5.4 mL Nuclei PURE 2M Sucrose Cushion solution and 0.6 mL Nuclei PURE Sucrose Cushion Solution; Sigma, MO, USA) to dilute the lysis buffer. The sample was then subjected to a one-step continuous sucrose cushion for further purification. Each sample was divided into two tubes, with 900 µL of the homogenized sample carefully layered over 500 µL of 1.8 M sucrose cushion solution. Blood-fed samples were divided into four tubes. Centrifugation was carried out at 13,000 × g for 45 min at 4°C. The supernatant was gently discarded, and the resulting pellet, containing the nuclei, was washed twice with washing buffer (1% BSA in PBS + 0.2 u Protector RNase Inhibitor; Sigma, MO, USA). All solutions were kept on ice during the procedure. Finally, the nuclear suspension was stained with 0.4% trypan blue, and the nuclear yield was estimated using a hemocytometer. Nuclei quality was accessed through light microscopy by the evaluation of nuclei membrane integrity. The targeted concentration of nuclear suspension was 700–1200 nuclei per microliter. Two biological replicates of the naïve and P. berghei mediated priming samples were collected. One biological replicate was performed for the control PBS-injected, bacteria-injected, sugar-fed, and blood-fed samples. For the detailed protocol please refer to Carvalho et al.49.

Chromium 10X snRNA-seq library preparation and sequencing
After performing the single nuclei isolation of the fat body of An. gambiae female mosquitoes with different stimuli, an estimated number of 10,000 fresh single nuclei were immediately used for 10X Genomics Chromium droplet snRNA-seq. Single nuclei encapsulation, cDNA synthesis, and libraries preparation were carried out using the Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (10x Genomics, Pleasanton, CA), following the manufacturer’s instructions. Assessment of cDNA and library quantification and quality was performed using 4200 Agilent TapeStation System with a High Sensitivity D5000 ScreenTape (Agilent, Santa Clara, CA). Paired-end sequencing was performed with Illumina NextSeq 550 or NextSeq 2000 platforms.

Single-nucleus RNA-seq processing and ambient RNA correction
To generate the single-nuclei gene expression matrix, raw sequencing reads were aligned to the Anopheles gambiae PEST (AgamP4.14, v59) reference genome using the Cell Ranger pipeline (version 7.0.0, 10× Genomics, Pleasanton, CA). The reference genome and corresponding annotation file were obtained from VectorBase (https://vectorbase.org/common/downloads/Current_Release/AgambiaePEST). The gene annotation (gtf) file was filtered to retain protein-coding genes, and a custom reference was built using the “cellranger mkref” function. Alignment and quantification were performed with the “cellranger count” function, using the parameters “--include-introns=true” and “--expect-cells=10000”, to generate both raw and filtered gene-barcode matrices. As nuclei isolation can result in the presence of ambient RNA—potentially from lysed cells—that becomes incorporated into droplets containing intact nuclei, we applied ambient RNA correction to the dataset. Correction was performed in each sample using the decontX algorithm from the celda package (version 1.20.0)50, which models and removes contamination from ambient background RNA in snRNA-seq data.

Quality control and cell filtering
Quality control and cell filtering were performed using the Seurat R package (version 4.2.1). For each sample, nuclei were retained based on sample-specific thresholds for the number of detected genes (Features), the percentage of mitochondrial gene expression, and the percentage of ribosomal gene expression, to remove low-quality nuclei and potential doublets. Cells were included if they expressed between 200 and 2000 genes for most samples (Naïve 1, Naïve 2, Prime 1, Prime 2, and Blood-fed), with adjusted upper bounds for PBS-control, bacteria-injected (2500), and Sugar-fed samples (1500). To minimize the inclusion of damaged or stressed nuclei, cells with a mitochondria gene percentage exceeding 2.5% (Naïve 1 and Prime 1), 1.5% (Naïve 2 and Prime 2), 7% (PBS-control), 5% (Bacteria-injected), or 10% (Sugar-fed and Blood-fed) were excluded. Additionally, a ribosomal gene expression threshold was applied, with upper limits ranging from 10% to 25% across samples to account for potential variation in ribosomal content between conditions. Cellular complexity, assessed using the log10 Genes Per UMI metric, was also evaluated to ensure transcriptomic diversity and minimize amplification bias; all retained nuclei had a complexity score greater than 0.75. These filtering parameters were chosen to optimize the retention of high-quality nuclei while minimizing technical artifacts and ambient RNA contamination.

Integrative analysis, cell clustering, and marker identification
Seurat-based integration and clustering analysis was performed on previously described datasets: Naïve and Prime, PBS-control and bacteria-injected, and Sugar-fed and Blood-fed. This was achieved by first log-normalizing the gene counts of the remaining filtered nucleus using the “NormalizeData” function. Next, the top 2000 most variable genes were identified for each sample using the “FindVariableFeatures” function and tested for suitability as integration anchors using the “FindIntegrationAnchors” function. A final set of 2000 anchors were then used in the “IntegrateData” function to integrate the samples into a single dataset. Following integration, the dataset was scaled using the “ScaleData” function. Dimensionality reduction was performed using “RunPCA”, and the first 30 principal components were used to generate a two-dimensional embedding with “RunUMAP”. Clustering was conducted with “FindNeighbors” and “FindClusters”, applying a resolution 0.3 to identify transcriptionally distinct cell populations. Marker genes for each cluster were identified using “FindAllMarkers” function in Seurat, requiring that genes must be detected in at least 25% of cells of the integrated dataset, an average log2 fold change (Avg log2FC) ≥ 0.5 and, an adjusted p-value (Pvaladj) < 0.05. Differential gene expression analysis was performed using the “FindMarkers” function to assess transcriptomic changes between treatment groups. For comparisons between PBS-control and bacteria-injected, as well as Sugar-fed and Blood-fed samples, stringent thresholds were applied, an average log2 fold change (Avg log2FC) ≥ 1 or ≤ −1 and an adjusted P value (Pvaladj) < 0.001. A more permissive threshold, Avg log2FC ≥ 0.5 and P val adj < 0.05, was used for comparisons between P. berghei-infected and naïve samples. Raw sequencing data have been deposited in the SRA database under BioProject accession number PRJNA1288431. The estimated relative cell abundance provided in Supplementary data 2 represents the relative proportion (%) of nuclei from each cluster relative to the total nuclei captured in each sample.

RNA velocity analysis
RNA velocity analysis was performed by first generating spliced and unspliced transcript counts from 10x CellRanger outputs using velocyto v0.17.1551. Velocities were then computed and projected onto the Seurat-derived UMAP embeddings using scVelo v0.2.552 and its dynamical model. Data were preprocessed, filtered, and normalized with “pp.filter_and_normalize” (min_shared_counts=30, n_top_genes=2000), followed by computation of first and second moments using “pp.moments” (n_pcs=30, n_neighbors=30). Finally, “scv.tl.recover_dynamics”, “scv.tl.velocity”, “scv.tl.velocity_graph”, and “scv.tl.recover_latent_time” were used to model transcriptional dynamics, estimate RNA velocities, construct directed cell-state transition graphs, and order cells along a latent developmental timeline.

Bulk RNA-seq RNA extraction, library preparation, and sequencing
Body wall tissues (abdominal fat body attached to the cuticle) were dissected from adult female mosquitoes as previously described. Each biological replicate consisted of a pool of 10 tissues (n=3 or 4). Dissected tissues were immediately transferred to 300 µL of TRIzolTM reagent (Thermo Fisher Scientific, MA, USA) and stored at −80°C until processing. Tissues were homogenized using an electric homogenizer, followed by the addition of 700 µL of TRIzolTM reagent. Samples were centrifuged at 12,000 × g for 5 min at 4°C to pellet the cuticle. After a 5-min incubation at room temperature, 200 µL of chloroform was added, and samples were vortexed briefly. Following a 3-min room temperature incubation, samples were centrifuged at 12,000 × g for 15 min at 4°C. The aqueous phase was transferred to a fresh 1.5 mL microcentrifuge tube, and the RNA was precipitated by adding 500 µL of isopropanol. Samples were gently mixed by inversion, incubated on ice for 15 min, and centrifuged at 12,000 × g for 15 min at 4°C. The resulting pellet was washed with 1 mL of 75% ice-cold ethanol, briefly vortexed, and centrifuged at 7500 × g for 5 min at 4°C. Supernatant was discarded, and pellets were air-dried before resuspension in 30 µL of nuclease-free water. RNA was homogenized and incubated at 55°C for 5 min to enhance solubilization. Final RNA samples were stored at −80°C. RNA libraries were prepared with NEBNext Ultra II RNA Library prep kit (New England Biolabs), and sequencing was performed on Illumina NovaSeq 6000 sequencer (150bp paired-end reads) by Novogene (Sacramento, CA). Raw sequencing data have been deposited in the SRA database under BioProject accession number PRJNA1288431.

Bulk RNA-seq data and statistical analysis
The quality of raw Illumina reads was checked using the FastQC tool (version 0.12.0) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Low-quality sequences with a Phred quality score (Q) below 20 and the Illumina adaptors were removed using TrimGalore v.0.6.7 (https://github.com/FelixKrueger/TrimGalore). High-quality reads were aligned to the Anopheles gambiae PEST (AgamP4.14, v59) transcriptome using RSEM v.1.3.3 (Li and Dewey 2011) with Bowtie 2 as the aligner for transcript quantification. The reference transcriptome was obtained from VectorBase (https://vectorbase.org/common/downloads/Current_Release/AgambiaePEST). Downstream analysis, including multi-dimensional scaling (MDS) plots and pairwise differential expression analysis were carried out with the edgeR package for R (version 3.19). For the differential expression analysis, the raw read counts of each transcript were used as input for edgeR, followed the standard TMM-normalization. Differentially expressed transcripts were identified using a negative binomial model, with log-fold-change (logFC) serving as the effect size and false discovery rate (FDR) controlling for multiple testing. Transcripts were considered differentially expressed if |logFC| ≥ 1 and FDR < 0.05. For comparisons between naïve and P. berghei-challenged samples, a relaxed threshold of logFC ≥ 0.5 with FDR < 0.05 was applied.

In situ hybridization (ISH)
Female mosquitoes were cold-anesthetized and placed on a drop of PBS. The abdomens were detached from the thorax and the midgut, ovaries, and Malpighian tubules were carefully removed from the body cavity. The body wall, including the abdominal fat body attached to the cuticle, was then incised along the lateral side using fine scissors. The dissected tissues were fixed for 1h in 4% paraformaldehyde on a 9-well plate at room temperature. After fixation, the tissues were gently transferred to a 1.5 mL microcentrifuge tube and subjected to a series of washes. Tissues were washed three times for 5 min each with 1 mL of PBST (0.1% Triton X-100 in PBS), with gentle rocking at room temperature. To reduce lipid content, the tissues were washed with increasing concentrations of methanol (PBST: Methanol; 7:3, 1:1, 3:7) for 5 min at room temperature. An absolute methanol wash was then performed for 10 min, after which the tissues were rehydrated by washing with decreasing concentrations of methanol (PBST: Methanol; 3:7, 1:1, 7:3) for 5 min each. Finally, the tissues were washed four times with PBST for 5 min each. ISH was performed using the RNAscope Multiplex Fluorescent Reagent Kit v2 assay (ACDBio, Abingdon, United Kingdom), adapted from the protocol described by [21]. Tissues were permeabilized with Protease Plus, provided by the kit, for 30 min at room temperature. Following two washes with PBST for 5 min, hybridization was carried out for 2h at 40°C. Amplification of the hybridized probes signal step was performed in sequential steps: AMP1 and AMP2 for 30 min each and AMP3 for 15 min, all at 40°C. Between each amplification step, tissues were washed twice with wash buffer for 2 min at room temperature. Fluorescent signal development was achieved using the TSA-based Opal 4-Color Automation IHC kit (PerkinElmer, MA, USA). RNA probes specific to the target genes were synthesized by ACDBio (Abingdon, United Kingdom). Fluorescent labeling was carried out stepwise, with the addition of corresponding HRP (C1, C2, C3, or C4) for 15 min at 40°C. Following each HRP incubation, tissues were washed twice for 2 min before incubation with the Opal fluorophore for 30 min at 40°C. After each fluorophore incubation, tissues were washed twice for 2 min, and HRP activity was blocked by a 15-min incubation with HRP blocker at 40°C. For morphological visualization, tissues were stained with Hoechst (1:10,000) to label the nuclei, and BODIPY 488 nm (1:5000 – Thermo Fisher Scientific, MA, USA) to visualize neutral lipids in wash buffer for 10 minutes. Following a final wash to remove excess Hoechst and BODIPY, slides were mounted in a drop of ProLongTM Gold Antifade Mountant (Thermo Fisher Scientific, MA, USA). Tissues were gently positioned and flattened in Prolong under a dissecting microscope to avoid folding or overlapping, and slides were allowed to dry overnight at room temperature in the dark.

5-ethynyl-2’-deoxyuridine (EdU) assay and Immunofluorescence
Female An. gambiae mosquitoes, 4–5 days post-eclosion, were blood-fed on 3-to 4-week-old female BALB/c uninfected mice (Charles River, Wilmington, MA, USA) for 15 min. Prior to feeding, mosquitoes were starved for ~16h. 5-ethynyl-2’-deoxyuridine (EdU – Thermo Fisher Scientific, MA, USA) 3 mg/mL 69 nL injections were performed at 6h post-feeding. Only fully engorged females were selected and maintained on 10% karo syrup following feeding until sample harvesting. Same-age female mosquitoes were also injected with 69 nL of EdU 3 mg/mL and maintained on ad libitum 10% karo syrup were used as a sugar-fed control. Abdominal fat body tissues were dissected 24h post-blood meal or 18h post EdU injection, along with their age-matched sugar-fed control mosquitoes. Body walls were dissected in PBS and immediately placed in 4% paraformaldehyde for 30 min at room temperature, briefly transferred to ice-cold 80% ethanol for 3 min, and fixed again in 4% paraformaldehyde for 1h at room temperature. Tissues were washed three times with PBST 0.1% (1× PBS, 0.1% Triton X-100) for 10 min each. EdU was developed as per manufacture instruction with the Click-iT Plus Plus EdU Imaging kit (Thermo Fisher Scientific, MA, USA). Briefly, the Click-iT Plus reaction (1× Click-iT reaction buffer, Copper protectant, Alexa Fluor picolyl azide 594 nm, 1× Click-iT EdU buffer additive) was prepared and incubated with the sample for 30 min at room temperature. Tissues were washed three times with PBST 0.1% for 5 min. After this, tissues were blocked for 2h at room temperature with blocking buffer (1× PBS, 0.1% Triton X-100, 2% BSA, 0.1% gelatin). Primary antibodies were diluted in blocking buffer: anti-phospho-histone H3 (pH3) (Ser10) (Millipore Sigma, MA, USA) 1:1000 or anti-Leucin rich protein 8 (LRR8) (Pacific Immunology, CA, USA) 1:100 and incubated overnight at 4°C. The next day, body walls were washed three times with blocking buffer for 10 min and then incubated with goat anti-rabbit immunoglobulin G (IgG)–Alexa Fluor 555 nm (Invitrogen, CA, USA) (1:1000) diluted in blocking buffer at room temperature for 2h. Tissues were washed twice in blocking buffer and then twice more in PBST 0.1%. Finally, tissues were incubated with Hoechst 405 nm 1:10,000 and BODIPY 488 nm 1:5000 and Phalloidin 647 nm (1:40) before being placed in ProLong Gold mounting media (Invitrogen, CA, USA).

Confocal microscopy and image processing
Confocal images were captured using a Leica TCS SP8 (DM8000) confocal microscope (Leica Microsystems, Wetzlar, Germany) with either a 20× or a 40× oil immersion objective equipped with a photomultiplier tube/hybrid detector. Leica Application Suite X (LAS X) software was used to capture microscopy images. Abdominal fat bodies were visualized with a white light laser, using 488 nm excitation and 52 nm emission for BODIPY 488 dye (neutral lipids), 550 nm excitation and 570 nm emission for Opal 570 (ISH probes), 588 nm excitation and 620 nm emission for Opal 620 (ISH probes), 676 nm excitation and 690 nm emission for Opal 690 (ISH probes), and a 405 nm diode laser for nuclei staining (Hoechst 33342). Images were taken using sequential mode and variable z-steps. Image processing and merging were performed using Imaris version 10.0.0 (Bitplane, MA, USA) and Adobe Photoshop CC (Adobe Systems, CA, USA).

FAS mRNA quantification by in situ hybridization
To quantify the FAS mRNA fluorescence, we used the surface mode on Imaris version 10.0.0 (Bitplane, Concord, MA, USA). Using this method, we can calculate the fluorescence intensity per voxel for each object that is positive for the FAS staining (Source Data). As an input we used confocal images that were captures using the same laser power and gain between naïve and challenged tissues. To create a surface, we used confocal z-stack sections and first applied a threshold between 13 and 45 for the number of voxels. After the surfaces were generated, we applied a final filtering step removing volumes that were below 1024 µm3 to remove smaller volumes and other non-related surfaces that might be generated. The fluorescence intensity mean per voxel was calculated for every surface that was positive for the mRNA of FAS. Statistical analysis was performed with GraphPad prism (version 10) using unpaired Mann–Whitney two-tailed test from 6 to 7 fields per condition (n=106 oenocyte clusters in naïve samples; n=105 oenocyte clusters in challenge samples).

Statistical analysis and reproducibility
No statistical methods were used to predetermine sample size. Sample sizes were chosen based on prior literature and practical considerations. No data was excluded except during standard quality control for sequencing analyses. Experiments were not randomized, and investigators were not blinded during experiments or outcome assessments. Single-nucleus RNA-seq analyses were performed in Seurat using predefined thresholds for gene detection, effect size, and adjusted P values, with multiple testing controlled by the Benjamini–Hochberg method. Differential expression analyses used nonparametric tests implemented in Seurat, with comparison-specific significance thresholds as described in the Methods. RNA velocity analyses were conducted using velocyto and scVelo with the dynamical model, without additional hypothesis testing. Bulk RNA-seq differential expression was analyzed using edgeR following TMM normalization, modeling counts with a negative binomial distribution and applying false discovery rate correction. Fluorescence intensity from in situ hybridization experiments was quantified using Imaris, and statistical comparisons were performed using unpaired two-tailed Mann–Whitney tests in GraphPad Prism 10. Exact sample sizes, statistical tests, and significance thresholds are provided in the figure legends and Supplementary Tables. All experiments were conducted using consistent parameters to ensure reproducibility.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General review\nThis manuscript presents a comprehensive single-nucleus RNA-seq atlas of Anopheles gambiae abdominal body wall–\nassociated tissues under various physiological and immune challenge conditions, including blood feeding, bacterial\ninjection, and Plasmodium infection. An innovative delipidation-based nuclei isolation protocol facilitated snRNA-seq\nanalysis of this lipid-rich tissue. The authors effectively integrate snRNA-seq with extensive RNA FISH to characterize six\nmajor abdominal fat body cell types, with a particular focus on trophocyte subpopulations and oenocytes, verifying condition-\nspecific responses. The study highlights the role of lipid-specialized oenocytes in immune priming during Plasmodium\ninfection. Additionally, it demonstrates transcriptional responses of trophocyte subpopulations to bacterial challenges,\naccompanied by hemocyte expansion. Moreover, the research reveals that blood feeding induces hemocyte proliferation\nand a reversible trophocyte transition into a vitellogenic state. This study is an in-depth characterization of the cellular\ncomposition of mosquitoes fat body tissues and their role upon infection, making a significant\ncontribution to our understanding of mosquito physiology and immunity. It employs state-of-the-art\ntechniques for single-nuclei analysis and functional validation; the manuscript is well-written and data\nwell presented.\n\nMajor comments\n1- The study aims to characterize Anopheles gambiae fat body responses to bacterial and Plasmodium\nberghei infections. However, the authors do not provide evidence of successful infections or the\nprevalence of infection in the mosquitoes used for snRNA-seq, bulk RNA-seq, and RNA FISH. Including\ndata on infection rates, such as oocyst/sporozoite counts or parasite qPCR for Plasmodium, and CFU or\nqPCR for bacteria at sampled time points, would clarify the prevalence of infection in each condition\nand strengthen the study.\n2- For bacterial infections, authors might provide justifications as to (1) Why bacteria were introduced\nvia injection rather than through a natural exposure, which might better mimic natural infection\nprocesses and (2) why a mixture of 2 bacterial strains was employed over a single strain infection.\nClarifying where the bacteria were injected and discussing any potential differences in immune\nresponse compared to natural gut passage would provide valuable context. Determining if bacteria\ncan be detected within the fat body itself would also enhance the understanding of localized infection\ndynamics.\n3- Have the authors considered the possibility of detecting pathogen-derived transcripts, in addition to\nhost transcript, in snRNA-seq and bulk RNA-seq datasets ? Furthermore, RNA FISH could be used for\nparasite or bacteria dectection to confirm the presence of pathogens and validate infection status in\nthe study.\n4- In reference to the statement about FAS expression being \"highly induced in response to priming in\noenocytes,\" the authors should consider providing quantification of their ISH measurements.\nIncluding statistical analyses would strengthen claims by offering more than visual intensity differences,\nparticularly for validating changes seen in Figure 3.\n5- The authors specify the concentration of the nuclei suspension used for sn-RNAseq, but some\nadditional key information would be interesting to share : (1) what is the yield of the nuclei isolation,\ni.e. the number of nuclei retrieved compared to the total number of expected nuclei ? (2) How did the\nauthors verify nuclei integrity and quality ?\n6- In Figures 6G-H, authors write « A few hemocytes were also EdU+ in both sugar-fed and blood-fed\nmosquitoes ». Could the authors please explain how they can identify those cells as hemocytes in the\nabsence of specific staining ?\nMinor comments\n7- Given the context that \"transcriptional differences between trophocyte clusters are more subtle and\nunique markers cannot be used to identify specific subpopulations,\" the authors might consider using\ntrajectory or velocity analysis, to explore whether these clusters represent specific subtypes or\ncontinuous states.\n8- The authors should consider employing differential expression analysis by using pseudo-bulk\nmethods, such as DESeq2 or edgeR, on the single-nucleus RNA-seq data. This approach could help\nidentify the same upregulated genes observed in bulk RNA profiles under conditions of Plasmodium\ninfection, bacterial challenge, or blood feeding.\n9- In Figure 1, panel A illustrates an overview of the experimental workflow, including both snRNA-seq\nand bulk RNA-seq. However, the rest of Figure 1 only presents results from snRNA-seq. It would be\nbeneficial to include at least a general result from the bulk RNA-seq to align with what is indicated in\npanel A. In the legend for panel C, the statement \"3 representative\nmarker genes per cluster\" is inaccurate for T2, which only includes 2 markers.\n10- While the authors indicate that Galactokinase (GALK) serves as a marker for all trophocyte clusters\n(line 122-124), it does not appear highly expressed in T5 (Figure 1C, Figure 2B). This observation\nweakens the claim that GALK is a universal marker across all trophocyte profiles, regardless of blood\nfeeding status. This, this claim could be moderated.\n11- Fatty acid synthase (FAS) has multiple isoforms in some mosquito species. Could the authors clarify\nthe specific FAS gene or isoforms measured in Anopheles gambiae? Are certain FAS isoforms uniquely\nenriched in oenocytes (line 138-\n139), or are they distributed across various cell types, including\ntrophocytes? Understanding the specialization of FAS in oenocytes would provide valuable insights.\n12- The results of the bulk transcriptomic analysis after bacterial challenge (lines 208–222), including\nthe regulation of antimicrobial, immune, and metabolic pathways, are mentioned only in a\nsupplementary table. The authors might consider adding a main or supplementary figure to enhance\nthe impact and accessibility of these findings. In addition, the strong transcriptional response to blood\nfeeding, (lines 286-310) is currently presented only in supplementary tables, which\ndiminishes their impact. To enhance clarity and strengthen these findings, the authors might consider\nadding them in a main or supplementary figure.\n13- In Figure 2A, the authors might add another item in the legend to specify what the grey boxes\nbehind the central and dorsal tissues represent.\n14- Would the author be able to clarify which study or facts supports the following claim : “lipids\ninterfere with cell dissociation and 10X single-cell partitioning and Gel Bead-in-Emulsion (GEM)\nformation » ?\n15- The authors might want to include a definition of endoreplication in their discussion.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "In this article the authors present a well-executed set of experiments at single-nucleus resolution to characterise the\ncomplexity of the mosquito fat body—an organ central to both female reproductive development and innate immunity. The\nwork is relatively descriptive in nature, as is often the case with this approach, but the level of cellular and molecular\ncomplexity revealed is valuable and will provide an important reference point for the field. It is therefore likely to be frequently\ncited.\n\nThe introduction is exemplary in its clarity. However, both I and a more junior colleague who helped me review the\nmanuscript felt that some important detail was missing from the description of the experimental set-up used to examine the\neffects of immune priming. This is a field in which the authors have made many of the seminal contributions—indeed, they\neffectively “wrote the book” on the subject—so the rationale deserves to be laid out more fully. The hypotheses underpinning\nthese experiments could be stated more explicitly, and fewer assumptions should be made about the reader’s familiarity with\nwhy particular time points, bacterial delivery methods, and bacteria–Plasmodium combinations were chosen.\n\nA similar point applies to aspects of the discussion concerning transcript localisation and endoduplication. The\ninterpretations themselves are reasonable, but the arguments would be clearer with more explicit rationale and fewer\nassumptions about prior knowledge. A little more empathy for the reader in these explanations would substantially\nstrengthen the manuscript.\n\nMore detailed comments and queries are provided below:\nIn the results section there needs to be an explanation of why they looked at the conditions they did. It would be helpful to\nadd a justification of why both single nucleus and bulk sequencing was used (i.e. what different results were expected from\neach one). Also on the experimental rationale, the manuscript would be easier to understand if the authors add an\nexplanation of why systemic bacterial challenge by injection into the hemolymph is an important and relevant immune\nchallenge to mosquito biology, including explaining the choice of bacteria used in the challenge (perhaps representatives of\ngram positive/ negative?)\n\nSeveral sections of the results presented interesting but incomplete stories. Adding either further experiments or at least\nfuller interpretation of the observations with clearer recommendations for future work would greatly improve the clarity and\ninterest of the manuscript to a wider audience. These included:\n1. Does the trade-off between immunity and metabolism mentioned on p18 of the MS (lines 393-397) extend to measurable\nimpacts on reproduction (as explored for Drosophila in Gupta et al 2022 DOI: 10.1186/s12915-022-01328-w)? Importantly,\nhow does bacterial or Plasmodium challenge impact the DNA replication or expression of yolk protein genes in trophocytes?\n2. Vitellogenin mRNA localisation. A possible parallel with past observations in Aedes trophocytes entering the vitellogenic\nphase is mentioned (lines 412-420), with ribosomes relocating and plasma membrane changes. Figure 6 shows a clear\nlocalized expression of Vg mRNA in the trophocytes at the hemolymph side using in situ hybridisation. However, given that\nthe single cell results were based on single nuclei, one might expect that Vg mRNA would additionally be enriched in the\nnuclei of these cells compared to the other cell types, since transcripts outside of the nucleus should have been\nremoved during nucleus preparation and their data processing steps to remove ambient mRNA. In figure 6 Vg mRNA cannot be seen\nin trophocyte nuclei. It should be explained why this this is. Is this because the level in the nuclei is low compared to the\nstrong cytoplasmic accumulation, or is it due to contamination of the sequenced nuclei with non-nuclear mRNA?\nFurthermore, there was a general issue with all in-situ microscopy presented that there was no marker for the cell membrane\nor sub membrane cytoskeleton, making it impossible to interpret where the cell margins were. Adding a cell\noutlining marker would really aid interpretation of these images for the reader and make the cell specific markers much more convincing.\n3. More explanation of how cells were counted is needed. A fuller explanation of the rationale for counting cell types based\non single nucleus sequencing and the error of these measurements (where replicate experiments allow this to be estimated)\nshould be added to the methods section. This is needed to validate the many interesting observations on the changes in the\nproportions and numbers of cell typed during different treatments. For example, the story regarding circulating versus static\ngranulocytes. In the introduction, lines 61-67 mention that immune priming in An. gambiae is mediated by release of\nhemocyte differentiation factor when microbiota contacts the midgut epithelia, and that this triggers an increase in the\nproportion of circulating granulocytes. Theoretically a changing proportion between circulating and fat-body\nassociated granulocytes could be achieved either by release of fat body granulocytes to the haemolymph (resulting in fewer fat body\ngranulocytes but more circulating), or by cell division which would boost the circulating population leaving the fat-body\nassociated population at the same size. These alternative hypotheses could have been investigated using their combined\nsingle nucleus followed by in situ hybridisation methods. In their investigation of the response of fat body associated\ngranulocytes to P. berghei challenge there was a clear transcriptional change but crucially, they state that they did not\nobserve a significant difference in the number of body wall-associated granulocytes (results lines 181-182). This leaves the\nreader wondering how they measured the number of granulocytes. Was this based on the relative proportion of nuclei with a\ngranulocyte expression profile compared to other fat body cell types in the single nuclei sequencing? If this is the case, what\nis the quality control for this – how do we know that the expression and physiological changes associated with the P.\nberghei challenge (or any of the other treatments used in the manuscript) did not influence the recovery rate of nuclei from\ndifferent cell types differently and bias the count? Can this observation be confirmed using HCR-FISH and counting fat body\nassociated granulocytes based on cell-type specific markers in naïve vs challenged mosquitoes? Would the final\ninterpretation of this result be that the boost to circulating granulocytes is not by release of fat body associated granulocytes?\nThis issue of how cells of each type were counted and how confident we can be in the counts/ relative proportions going on\nsingle nucleus clustering alone is also relevant for the stated observations about trophocytes (lines 263-265),\ngranulocytes following bacterial challenge (lines 356-360) and oenocytes (p369-371).\n4. The story regarding endoreplication in trophocytes was very interesting but seemed to finish before reaching a firm\nconclusion. They observed EdU-positive but pH3 negative trophocytes which would be consistent with DNA replication\nwithout mitosis. Why did they not attempt to look at the ploidy in these cells? This could be done using for example FACS or\neven just measuring the brightness of nucleic acid stains such as Hoechst using microscopy. Similarly, given that they\napplied a filter to removed doublets from single nucleus data, can they be sure they did not accidentally filter out\ntrophocytes with a higher ploidy – how did the various QC metrics differ between potential doublets\nand endoreplicated nuclei?\nFurthermore, in this study they only investigated the response of the fat body to the first blood meal. If the endoreplication\noccurs after the first blood feeding to boost transcription, this raises questions as to what happens to ploidy after 6 days\nwhen the transcriptional profile returns to pre-blood fed patterns, particularly in their cluster “T1” – are all these extra DNA\ncopies kept? Or perhaps could the “T1” cluster 6 days after blood feeding be new cells and not the same T5 endoreplicated\ncells returning to pre-blood fed transcriptional patterns? If the cells in T1 are the T5 cells and ploidy remains at this boosted\nlevel, does that mean that after subsequent bloodmeals the transcriptional shift would be different since it should no longer\nrequire genes involved in DNA replication since the endoreplication has already happened?\n5. On the report of the result that hemocytes are the second most abundant cell type (lines 124-125)”Surprisingly, hemocytes\n(Hm1, Hm2) are the second most abundant cell type, representing 7.4% of the cells (Supplementary Table 2)”, it needs to be\nexplained why this is surprising?\n6. The question of whether cells are “resident” or “associated” with the fat body does raise the question of whether they could\nmove in and out. This is particularly relevant to the topic of the parallels between insect fat body and the vertebrate immune\nsystem (e.g. the potential role of T4 trophocytes in immune surveillance discussed lines 391-393), a topic that is of strong\ninterest to the general reader. They postulate in lines 356-357 “We observed a doubling of the number of fat body-associated\ngranulocytes in response to bacterial challenge, suggesting either proliferation of resident cells and/or recruitment from\ncirculation”. Although directly testing the alternative hypotheses of cell movement vs cell proliferation might be beyond the\nscope of this study, this question should be better highlighted in the discussion with a recommendation as to what sort of\nexperimental approach could definitively answer this question in future studies (for example tracking of cells over time).\nFurther discussion of how the Anopheles fat body is similar or different from other insects would have improved the\ndiscussion section. This seems part of the stated aim of addressing a knowledge gap in Anopheline mosquitoes about the\ncellular composition and physiology of the fat body. The discussion section alludes to studies of the fat body cellular\ncomposition in other organisms including Drosophila melanogaster and Bombyx mori, and yet there is no discussion of the\nsimilarities and differences between what they observed in Anopheles gambiae fat body single nucleus sequencing and\nthese earlier works.\n\nThere are a few technical considerations. As stated by my colleague above, the manuscript appears to be generally\ntechnically sound, but with the rationale of experiments, some technical issues warranted a clearer explanation. In the\ndiscussion the authors claim to have overcome technical barriers to nucleus isolation (line 330, 432). They give no details of\nthese technical barriers and don’t show any quality control of the nuclei isolated prior to sequencing (for example\nmicroscopy to show nuclei morphology). They seem to have followed initially a protocol similar to what was used in\nGupta and Lazzaro 2022 (https://doi.org/10.1080/19336934.2021.1978776) followed by using the Sigma “Nuclei Pure Prep”\nstandard protocol. Were other options for nuclei isolation tried and worked less well? As a minimum, images of the isolated\nnuclei for each treatment and yield as expressed as amount of nuclei/number of tissues dissected should be shown. It could\nalso be acknowledged that Anopheles oenocytes have been isolated in the past (Grigoraki et al 2020, 10.7554/eLife.58019),\nalthough this was with the help of a transgenic GFP expressing line whereas the method developed in the current\nmanuscript are applicable to all genotypes.\n\nThe number of biological replicates used for the single cell work was very low. Supplementary figure 1G suggests 2* “naïve”,\n2* “challenge”, and only one each of other conditions (although “control” might be the same as one of them?). This low level\nof replication is likely due to the high cost of undertaking the single cell work but means that any important observations such\nas on the relative numbers of cells in each cluster need to be confirmed by another method for statistical robustness.\n\nOn the analysis of the single nucleus sequencing data, lines 102-103 state that the “cutoffs for the number of counts and\nfeatures, as well as the percentage of mitochondrial and ribosomal content, were set for each sample (Supplementary Figure\n1)”. However, although supplementary figure 1 shows the spread of the data it does not actually indicate what cutoffs were\nused or how the decision to set these cutoffs was made. This should be made clear.\n\nThe selection of cluster specific markers seemed to have a very low bar in terms of the fold change (Avg log2FC > 0.5) – just\n1.41 times more expression in the enriched clusters? The authors should explain how this cut-off was decided on. In\nsupplementary table 1 where these markers are described, the proportion of cells with detectable expression was also rather\nlow for some marker genes.\n\nLines 531-533 mention that “The gene annotation (gtf) file was filtered to retain protein-coding genes”. Could valuable data\non the expression of non-protein coding genes such as long noncoding RNAs have been discarded at this point and how\nwould its inclusion have affected the clustering?\n\nFor the ambient RNA correction (lines 537-538 “Correction was performed using the decontX algorithm from the celda\npackage [48]” it should be specified if the same model was applied across all samples or whether this was modelled\nindividually in each sample.", "reviewer_4": "de Carvalho and coworkers present a single nuclei RNAseq data set of Anopheles gambiae fat body. They show a variety of\ncell types, and notably assign five trophocyte clusters. They show some cells responding to bacterial challenge and, not\nsurprisingly see a strong upregulation of vitellogenesis-related genes after a blood meal.\nThe introduction is beautifully written, the experiments are well described. The results show the expected heterogeneity of\ncell types in this tissue.\nThis is a well-done study.\nMinor points:\n- a scheme of where the different trophocyte types are located within the abdomen would be helpful.\n- could the tissue and subcellular locations of some mRNAs you see (especially Vg) be due to technical problems of the in\nsitu hybridization protocol? Just asking questions...\n- a longer discussion of how the results of [30] compare to these findings would be appropriate."}, "author_response": "Thank you for the positive feedback.\nPlease find below some major and minor comments that will hopefully help the authors to further\nimprove their work:\nMajor comments\n1- The study aims to characterize Anopheles gambiae fat body responses to bacterial and Plasmodium\nberghei infections. However, the authors do not provide evidence of successful infections or the\nprevalence of infection in the mosquitoes used for snRNA-seq, bulk RNA-seq, and RNA FISH. Including\ndata on infection rates, such as oocyst/sporozoite counts or parasite qPCR for Plasmodium, and CFU or\nqPCR for bacteria at sampled time points, would clarify the prevalence of infection in each condition\nand strengthen the study.\nResponse:\nThank you for pointing out the need to include the data we used to validate the Plasmodium berghei and\nbacterial infections. We have standardized protocols in our laboratory to validate both Plasmodium and\nbacterial infections, and this information is now included in the revised manuscript. We appreciate the\nopportunity to clarify these important aspects of our methodology.\nP. berghei infections: we used 2–3-week-old mice exhibiting parasitemia levels between 3–6% and an\nexflagellation rate of 1–2 events per microscopic field. These parameters were chosen to ensure a\nconsistent and robust infection in the mosquito midgut.\nMosquito immune priming experiments: Our study specifically focused on the priming response triggered\nby Plasmodium infection, with particular interest in the role of oenocytes in the fat body. Ookinete\ninvasion, in the presence of the gut microbiota triggers a systemic and permanent state of enhances\nimmunity. The establishment of the immune priming occurs within the first 24 hours post blood feeding,\nwhen ookinetes invade the mosquito midgut. Fat body tissue samples were collected 6 days post-infection\nto capture the long-term effect of priming on fat body trophocytes and oenocytes.\nTo confirm successful Plasmodium infection, we employ one of the two following strategies:\n1. Direct oocyst counts: A subset of 10–20 mosquitoes were kept alive, and midgut dissections were\nperformed at 10 days post feeding to directly count the number of oocysts/midgut, thereby\nvalidating the success of the infection. This method was used for the snRNAseq Exp #1 and the\nBulk-RNAseq experiments (see figure below).\nThe disadvantage of this strategy is that we need to collect the samples 6 days post-infection and\nstart the processing the sample before we know if the infection was successful (counting for P.\nberghei needs to be done 10-12 days post infection). To solve this problem, we can also use a\nmolecular marker of midgut invasion.\n2. Molecular marker: Serpin 6 expression is highly induced in ookinete-invaded midgut cells 24 h\npost-feeding and expression of this gene is a well-established marker of mosquito midgut infection\nby P. berghei ookinetes. The expression levels of Serpin 6 midgut mRNA 24 hours post feeding\nbetween control and infected midguts was compared as an indicator of ookinete invasion. This\nstrategy was used in snRNAseq Exp #2, and we confirmed that in that experiments Serpin 6\nexpression was more that 5,000-fold higher in midguts infected with P. berghei, than in control\nfemales fed on an uninfected mouse (see figure below).\nFor bacterial infections, we injected a mixture of a Gram-positive (Micrococcus luteus) and a Gram-\nnegative (Escherichia coli) bacterium directly into the mosquito hemolymph to induce a systemic immune\nresponse. The insect fat body is the primary site of synthesis of antimicrobial peptides and proteins\ncirculating in the hemolymph. Our rationale was to use a broad immune stimulus to activate systemic\nimmune responses and assess the corresponding activity in the mosquito fat body. Previous studies have\ndemonstrated that the fat body mounts a strong immune response to systemic infections [1]. This includes\nthe transcriptional activation of antimicrobial peptides (AMPs) genes, which are well-established markers\nof immune pathway activation.\nMosquitoes were systemically infected using a fine glass needle inserted into a soft area of the thorax.\nInjected bacterial are quickly cleared from circulation and there are no reports of bacteria directly infecting\ntrophocytes or other fat body-associated cell types.\nTo confirm that the fat body immune response was activated following the bacterial challenge, we\nmeasured the mRNA expression of some AMP genes by qPCR at 4 hours post-injection. We confirmed a\nstrong response with an increase in expression of defensin 1 (200-fold) and cecropin 1 (135-fold), two well-\ncharacterized AMPs, as well as lysozyme 1 (35-fold) confirming that the bacterial injection successfully\nstimulated the fat body’s immune response (results below).\nThese validation strategies to confirm the infections have been included in the methods section of the\nrevised manuscript: “A subset of 10 - 20 mosquitoes from each experiment was used to confirm the\ninfection success by either: (1) oocyst counts at 10 days post-infection, as a direct measurement of\ninfection; or (2) serpin 6 expression at 24h post-infection as marker associated with midgut invasion by P.\nberghei ookinetes.” was included under the topic “Plasmodium berghei infection” in the methods section.\nAnd the sentence: “Validation of immune activation of the fat body after bacteria injection was confirmed\nby qPCR of antimicrobial peptides at 4 hours post-injection.” was included under the topic “Bacteria\nchallenge” in the methods section.\n2- For bacterial infections, authors might provide justifications as to (1) Why bacteria were introduced\nvia injection rather than through a natural exposure, which might better mimic natural infection\nprocesses and (2) why a mixture of 2 bacterial strains was employed over a single strain infection.\nClarifying where the bacteria were injected and discussing any potential differences in immune\nresponse compared to natural gut passage would provide valuable context. Determining if bacteria\ncan be detected within the fat body itself would also enhance the understanding of localized infection\ndynamics.\nResponse:\nThese are relevant questions that are explained in more detail in the revised manuscript. We appreciate\nthe opportunity to expand on the rationale of our experimental design.\n(1) Our aim was to induce a broad systemic immune response and assess its direct impact on the fat\nbody tissue. We choose bacteria injection for several reasons:\nControlled dosage: Injecting bacteria allows us to deliver a standardized number of bacterial cells\ninto each mosquito, reducing variability in the immune response across individuals.\nDirect exposure: By introducing the bacteria directly into the hemolymph, we ensure that the\nobserved transcriptional changes in the fat body are the result of systemic immune activation.\nTissue specificity: An oral infection would primarily localize bacteria to the midgut, which is not\nthe focus of this study. We agree with the reviewer that the responses to oral infection are\nimportant, however, the midgut has its own immune defenses to ingested microbiota or\npathogens, which could introduce confounding variables and indirect effects on the fat body.\nWe used a sublethal bacterial load to activate the fat body response and we believe that a systemic\ninjection was the best way to address the question of whether all trophocytes respond the same\nto a systemic bacterial challenge or whether there is some level of specialization.\n(2) We used a combination of two bacterial strains—one Gram-positive (Micrococcus luteus) and one\nGram-negative (Escherichia coli)—to ensure broad activation of the major immune pathways in\nmosquitoes. Our goal was to provide a broad and robust immune stimulus capable of activating\nthe systemic immune response in the fat body, allowing us to assess which fat body–associated\ncell types respond.\nAs noted previously, bacteria were injected into the hemolymph through a soft spot in the\nthorax. Importantly, the thorax was not included in the dissections or in our downstream\nanalyses—only the abdominal fat body and body-wall associated tissues were examined. Thus,\nany immune response observed in the fat body reflects the systemic exposure to bacteria or\nbacterial immune elicitors circulating in the hemolymph, rather than local effects from the\ninjection site.\nBecause the fat body is bathed by the hemolymph, trophocytes and other tissues directly\nrespond to bacteria or immunogenic molecules released by bacteria.\nTo our knowledge, there are no reports describing bacteria inside trophocytes or other fat body–\nassociated cell types, except for phagocytic hemocytes, in mosquitoes. While some studies have\nshown bacteria in close proximity to pericardial cells in the periosteal region [2], these bacteria\nwere not observed within the cells themselves. Given that we injected bacteria into the thorax\nand excluded thoracic tissues from analysis, it is unlikely that bacteria were present within the\nfat body tissue examined in our study. Nonetheless, it is well established in several insect species\nthat immune activation in the fat body leads to antimicrobial peptide (AMP) expression and\nsecretion, which supports the use of AMPs gene expression as a readout for activation of\nimmune response in this tissue.\nWe have now clarified this point on the results section: “The fat body plays an important role in the\nsystemic immune response by producing antimicrobial peptides and contributing to pathogen\nmelanization. However, cell type-specific responses remain poorly understood. The transcriptional\nresponse of the abdominal body wall to bacterial infection was explored by challenging adult females\nwith a systemic injection of a mixture of a Gram-negative (E. coli) and a Gram-positive bacteria (M.\nluteus), or a sterile PBS control, and harvesting the abdominal body wall 4 hours later. This approach\nensured the direct delivery of a standardized bacterial load into the hemolymph, triggering a robust and\nphysiologically relevant immune response. The combination of Gram-positive and Gram-negative\nbacteria provided a broad immune stimulus, ensuring the activation of the major immune signaling\npathways.”\n3- Have the authors considered the possibility of detecting pathogen-derived transcripts, in addition to\nhost transcript, in snRNA-seq and bulk RNA-seq datasets? Furthermore, RNA FISH could be used for\nparasite or bacteria detection to confirm the presence of pathogens and validate infection status in\nthe study.\nResponse:\nThank you for suggesting this experiment.\nWe did not consider detecting pathogen-derived transcripts using either bulk RNA-seq, snRNA-seq, or\nRNA FISH from fat body tissues, as neither Plasmodium nor bacteria are expected directly associated\nwith the fat body tissue. Parasites remain within the midgut basal lamina and studies with labeled\nbacteria have shown that they are quickly filtered out by hemocytes associated with the periosteal\nregion of the mosquito heart. In another ongoing project, we are analyzing the responses of midgut\ntissues, and we are also analyzing the mRNAs from the parasites at the ookinete and oocyst stages.\n4- In reference to the statement about FAS expression being \"highly induced in response to priming in\noenocytes,\" the authors should consider providing quantification of their ISH measurements.\nIncluding statistical analyses would strengthen claims by offering more than visual intensity differences,\nparticularly for validating changes seen in Figure 3.\nResponse:\nThank you for suggesting statistical analysis of the RNA FISH images. We have now quantified the mean\nfluorescence intensity per oenocyte voxel in naïve and primed RNA FISH images (6-7 fields each) and\nincluded these results as Supplementary figure 4. The manuscript has been revised to emphasize the\nlocalization of FAS in oenocytes as revealed by ISH, and the quantification of FAS fluorescent signal\nintensity has been incorporated into the results section, that now reads: “Furthermore, we confirmed by\nISH that FAS expression induction in response to priming occurs specifically in oenocytes present in the\nlateral and ventral regions of the abdominal wall (Figure. 3C-D, Supplementary Figure 3C-F,\nSupplementary figure 4). The induction in expression of FAS observed in bulk RNA-seq was also\nconfirmed by ISH, which showed approximately a two-fold increase of FAS following priming\n(Supplementary figure 4). “\nWe have included the “FAS mRNA quantification by in situ hybridization” topic on the methods: “To\nquantify the FAS mRNA fluorescence, we used the surface mode on Imaris 10.0.0 (Bitplane, Concord,\nMA,USA). Using this method, we can calculate the fluorescence intensity per voxel for each object that is\npositive for the FAS staining. As an input we used confocal images that were captures using the same\nlaser power and gain between naïve and challenged tissues. To create a surface, we used confocal z-stack\nsections and first applied a threshold between 13 and 45 for the number of voxels. After the surfaces\nwere generated, we applied a final filtering step removing volumes that were below 1024 µm3 to remove\nsmaller volumes and other non-related surfaces that might be generated. The fluorescence intensity\nmean per voxel was calculated for every surface that was positive for the mRNA of FAS.”\n5- The authors specify the concentration of the nuclei suspension used for sn-RNAseq, but some\nadditional key information would be interesting to share: (1) what is the yield of the nuclei isolation,\ni.e. the number of nuclei retrieved compared to the total number of expected nuclei? (2) How did the\nauthors verify nuclei integrity and quality?\nResponse:\nThe reviewer brings up an important point. To our knowledge, there is currently no published estimate of\nthe total number of cells or nuclei in the mosquito abdominal fat body, which limits the ability to define\nan expected recovery yield. We worked for several months to optimize the recovery and quality of the\nnuclei. In our experience, sucrose cushion cleanup is the step with the highest loss of nuclei, but it is critical\nto remove debris and enrich for nuclei. We tested different amounts of starting material, different\nhomogenization times, conditions of delipidation and different time and centrifugation speeds for the\nsucrose cushion step. We were able to develop an optimized protocol to consistently retrieve thousands\nof nuclei from fat body cells (trophocytes and oenocytes) as well as other associated cell types such as\npericardial cells, hemocytes, epidermal cells, and neurons. We do not know the relative efficiency of\nrecovery of the different cell types, because the nuclei isolation and enrichment protocol may introduce\nsome bias, depending on the relative efficiency with which the nuclei from different cells are recovered.\nNuclei quality was verified by light microscopy, with attention to the preservation of DNA content and\nintegrity of nuclear membranes. A manuscript with a detailed description of our nuclei isolation protocol\nis currently under review in Bio-protocol that we believe will be very useful to the community.\nThe information regarding the assessment of nuclei quality has been added to the methods section of\nthe revised manuscript: “Nuclei quality was accessed through light microscopy by the evaluation of\nnuclei membrane integrity.”\n6- In Figures 6G-H, authors write « A few hemocytes were also EdU+ in both sugar-fed and blood-fed\nmosquitoes ». Could the authors please explain how they can identify those cells as hemocytes in the\nabsence of specific staining?\nResponse:\nPoint well taken. Morphologically, sessile hemocytes on the mosquito body wall are very small and have\nno detectable lipid droplets in their cytoplasm. They are typically localized on the surface of fat body\nlobes, often in close proximity to tracheas. Compared to trophocytes, hemocytes generally have much\nsmaller nuclei, which assists in their identification; however, we acknowledge that this distinction may\nnot always be clear as detection by immunofluorescence with hemocyte-specific antibodies.\nTo confirm that the observed EdU+ cell were indeed hemocytes, we performed immunofluorescence\nassays using an antibody against LRR8 (Leucine-rich repeat protein 8), a general hemocyte marker in\nblood-fed mosquitoes. We observed EdU incorporation in a subset of LRR8+ cells, indicating that blood\nfeeding stimulates DNA replication in hemocytes. A representative image of this co-localization is now\nincluded as Supplementary Figure 9. These results are included in the revised results section: “A few cells\nwith small nuclei were observed in both sugar-fed and blood-fed mosquitoes (Figure 6G-H, white\narrow). Notably, some of these small-nuclei cells were positive for the hemocyte marker LRR8\n(Supplementary Figure 9).”\nThe methodology to carry out EdU and immunofluorescence staining has been added to the methods\nsection of the revised manuscript:\n“5-ethynyl-2'-deoxyuridine (EdU) assay and Immunofluorescence (IFA)\nFemale An. gambiae mosquitoes, 4-5 days post-eclosion, were blood-fed on 3- to 4-week-old female\nBALB/c uninfected mice (Charles River, Wilmington, MA, USA) for 15 minutes. Prior to feeding,\nmosquitoes were starved for approximately 16 hours. 5-ethynyl-2'-deoxyuridine (EdU – ThermoFisher\nScientific, MA, USA) 3mg/mL 69nL injections were performed at 6 hours post-feeding. Only fully\nengorged females were selected and maintained on 10% karo syrup following feeding until sample\nharvesting. Same-age female mosquitoes were also injected with 69nL of EdU 3mg/mL and maintained\non ad libitum 10% karo syrup were used as a sugar-fed control. Abdominal fat body tissues were\ndissected 24 hours post-blood meal or 18 hours post EdU injection, along with their age-matched sugar-\nfed control mosquitoes. Body walls were dissected in PBS and immediately placed in 4%\nparaformaldehyde for 30 min at room temperature, briefly transferred to ice-cold 80% ethanol for 3\nminutes, and fixed again in 4% paraformaldehyde for 1 hour at room temperature. Tissues were washed\nthree times with PBST 0.1% (1x PBS, 0.1% Triton X-100) for 10 minutes each. EdU was developed as per\nmanufacture instruction with the Click-iT Plus Plus EdU Imaging kit (ThermoFisher Scientific, MA, USA).\nBriefly, the Click-iT Plus reaction (1x Click-iT reaction buffer, Copper protectant, Alexa Fluor picolyl azide\n594nm, 1x Click-iT EdU buffer additive) was prepared and incubated with the sample for 30 minutes at\nroom temperature. Tissues were washed three times with PBST 0.1% for 5 minutes. After this, tissues\nwere blocked for 2 hours at room temperature with blocking buffer (1x PBS, 0.1% Triton X-100, 2% BSA,\n0.1% gelatin). Primary antibodies were diluted in blocking buffer: anti-phospho-histone H3 (Ser10)\n(Millipore Sigma, MA, USA) 1:1,000 or anti-Leucin rich protein 8 (LRR8) (Pacific Immunology, CA, USA)\n1:100 and incubated overnight at 4 °C. The next day, body walls were washed three times with blocking\nbuffer for 10 minutes and then incubated with goat anti-rabbit immunoglobulin G (IgG)–Alexa Fluor\n555nm (Invitrogen, CA, USA) (1:1,000) diluted in blocking buffer at room temperature for 2 hours.\nTissues were washed twice in blocking buffer and then twice more in PBST 0.1%. Finally, tissues were\nincubated with Hoechst 405nm 1:10,000 and BODIPY 488nm 1:5,000 and Phalloidin 647 nm (1:40)\nbefore being placed in ProLong Gold mounting media (Invitrogen, CA, USA).”\nMinor comments\n7- Given the context that \"transcriptional differences between trophocyte clusters are more subtle and\nunique markers cannot be used to identify specific subpopulations,\" the authors might consider using\ntrajectory or velocity analysis, to explore whether these clusters represent specific subtypes or\ncontinuous states.\nResponse:\nThank you for suggesting the trajectory and RNA velocity analyses for our dataset.\nWe performed trajectory analysis using multiple tools, including Monocle 3, Slingshot, and TSCAN, both\nwith and without subsetting the trophocyte clusters. However, in all cases, the cells did not appear to\nrepresent continuous states in any of the analyzed samples.\nRNA velocity analysis was also not informative for distinguishing whether the clusters represent subtypes\nor continuous states. However, it revealed dramatic differences in the overall level of unspliced mRNAs,\nreflecting the dramatic differences in gene expression between sugar-fed and blood-fed samples. When\nthe transcriptional activity of a cell is very high, unspliced mRNAs accumulate in the nucleus. In some\ncells in clusters T1, T2 and T3 from sugar fed females are transcriptionally active, to maintain the basal\nfunction of fat body trophocytes as providing lipids as an energy source, synthesizing the major proteins\nand lipoproteins circulating in the hemolymph and maintaining a basal level of antimicrobial peptides.\n[see figure below, now Fig. S6 in the revised manuscript, in which dark blue indicates cells with very low\nlevels of unspliced mRNAs (low transcriptional activity) and dark red indicates high levels of unspliced\nmRNAs (high transcriptional activity)].\nIn blood-fed females, the number of cells in clusters T1 decrease dramatically (92%), with a less extreme\nreduction in clusters T2 (46%) and T3 (38%). The transcriptional activity in cluster T3 also is greatly\nreduced. A simultaneous increase in the number of cells in cluster T5 is observed, which have extremely\nhigh levels of unspliced mRNAs, reflecting very high transcriptional activity of genes involved in\noogenesis, such as vitellogenin, which undergoes a 4,000-fold increase in mRNA levels. High\ntranscriptional activity was also observed in the Hm1 hemocyte cluster in response to blood feeding.\nAltogether, these analyses reinforce our conclusion that blood feeding induces extensive transcriptional\nreprogramming, characterized by a shift from cluster T1 to cluster T5, and a strong transcriptional\nactivation in Hm1 hemocytes.\nRNA velocity analysis is now included as Supplementary figure 6 and a description of the results was\nincluded: “RNA velocity analysis of blood-fed samples indicated enhanced transcriptional dynamics\nwithin cluster T5, evidenced by an enrichment of unspliced transcripts and a predominance of positive\nvelocity vectors (Supplementary Figure 6).”\nRNA velocity analysis was also added to the methods section:\n“RNA velocity analysis\nRNA velocity analysis was performed by first generating spliced and unspliced transcript counts from 10x\nCell Ranger outputs using velocyto v0.17.15 [50]. Velocities were then computed and projected onto the\nSeurat-derived UMAP embeddings using scVelo v0.2.5 [51] and its dynamical model. Data were\npreprocessed, filtered, and normalized with “pp.filter_and_normalize” (min_shared_counts=30,\nn_top_genes=2000), followed by computation of first and second moments using “pp.moments”\n(n_pcs=30, n_neighbors=30). Finally, “scv.tl.recover_dynamics”, “scv.tl.velocity”, “scv.tl.velocity_graph”,\nand “scv.tl.recover_latent_time” were used to model transcriptional dynamics, estimate RNA velocities,\nconstruct directed cell-state transition graphs, and order cells along a latent developmental timeline.”\n8- The authors should consider employing differential expression analysis by using pseudo-bulk\nmethods, such as DESeq2 or edgeR, on the single-nucleus RNA-seq data. This approach could help\nidentify the same upregulated genes observed in bulk RNA profiles under conditions of Plasmodium\ninfection, bacterial challenge, or blood feeding.\nResponse:\nWe appreciate the reviewer’s suggestion to perform pseudo-bulk differential expression analysis using\nDESeq2 or edgeR. However, our single-nucleus RNA-seq dataset includes one biological replicate per\ncondition, which prevents robust estimation of biological variability required for pseudo-bulk methods.\nWe therefore compared mRNA expression in thousands of nuclei from individual cells and\ncomplemented our analysis using bulk RNA-seq from samples obtained in triplicate. This allowed us to\nconfirm and quantitate changes in gene expression in RNA isolated from the whole cells (nuclei and\ncytoplasm).\n9- In Figure 1, panel A illustrates an overview of the experimental workflow, including both snRNA-seq\nand bulk RNA-seq. However, the rest of Figure 1 only presents results from snRNA-seq. It would be\nbeneficial to include at least a general result from the bulk RNA-seq to align with what is indicated in\npanel A. In the legend for panel C, the statement \"3 representative marker genes per cluster\" is\ninaccurate for T2, which only includes 2 markers.\nResponse:\nPoint well taken, we have separated the experimental workflow diagrams for sn-RNAseq and Bulk-\nRNAseq, they now appear as Fig. 1A and Fig. 3A in the revised manuscript. We have also included more\ninformation on the bulk RNA-seq results in Supplementary figures 5 and 8 and corrected the figure\nlegend of panel 1C to reflect the number of genes markers for the T2 cluster in the dot plot.\n10- While the authors indicate that Galactokinase (GALK) serves as a marker for all trophocyte clusters\n(line 122-124), it does not appear highly expressed in T5 (Figure 1C, Figure 2B). This observation\nweakens the claim that GALK is a universal marker across all trophocyte profiles, regardless of blood\nfeeding status. This, this claim could be moderated.\nResponse:\nWe have modified the claim to state that Galactokinase (GALK) is a marker of sugar-fed trophocytes\ncluster. We have changed the sentence to: “Five distinct trophocyte transcriptional profiles were\nobserved, with clusters T1, T2, T3 and T4 present in sugar-fed samples and cluster T5 present only in\nblood-fed samples. Galactokinase (GALK - AGAP002914) is a general marker gene expressed in sugar-fed\ntrophocyte clusters (Figure 1C, Figure 2B).”\n11- Fatty acid synthase (FAS) has multiple isoforms in some mosquito species. Could the authors clarify\nthe specific FAS gene or isoforms measured in Anopheles gambiae? Are certain FAS isoforms uniquely\nenriched in oenocytes (line 138-139), or are they distributed across various cell types, including\ntrophocytes? Understanding the specialization of FAS in oenocytes would provide valuable insights.\nResponse:\nIn Anopheles gambiae PEST genome (AgamP4.14, v59), there are three annotated as fatty acid synthase\n(FAS) genes– AGAP001899, AGAP008468, and AGAP009176. The FAS genes AGAP001899 and\nAGAP008468 are both markers of oenocytes and are upregulated in primed mosquitoes based on the\nbulk RNA-seq data. The FAS gene AGAP009176 is not a specific marker of the clusters found in our\nsnRNA-seq dataset and it was not regulated in primed mosquitoes. We selected the gene AGAP001899\nas example to validate our results with qPCR and RNA FISH (FAS1899 probe), and we observed an\nincrease in gene expression in primed mosquitoes and a specific staining of oenocytes with the FAS1899\nprobe. The feature plots of the three FAS genes in our dataset are shown in the figure below, with\nAGAP001899 and AGAP008468 highly expressed in oenocytes (dark orange), while the AGAP009176\ngene is highly expressed in many other clusters.\n12- The results of the bulk transcriptomic analysis after bacterial challenge (lines 208–222), including\nthe regulation of antimicrobial, immune, and metabolic pathways, are mentioned only in a\nsupplementary table. The authors might consider adding a main or supplementary figure to enhance\nthe impact and accessibility of these findings. In addition, the strong transcriptional response to blood\nfeeding, (lines 286-310) is currently presented only in supplementary tables, which diminishes their\nimpact. To enhance clarity and strengthen these findings, the authors might consider adding them in a\nmain or supplementary figure.\nResponse:\nWe thank the reviewer for the suggestion. To enhance the clarity and impact of our bulk transcriptomic\nresults, we have added two figures (Figs. S5 and S8) combining volcano plots and heatmaps to display\nexamples of differentially expressed genes after bacterial challenge or blood feeding, highlighting the\nmagnitude and significance of transcriptional changes. These panels provide a concise and accessible\nrepresentation of the bulk RNA-seq results, which were previously only included in supplementary tables\n(Supplementary Figures 5 and 8).\n13- In Figure 2A, the authors might add another item in the legend to specify what the grey boxes\nbehind the central and dorsal tissues represent.\nResponse:\nThank you for your observation. We included the description of the gray boxes to the Figure 2A legend. It\nnow reads: “Gray boxes represent three segments of abdominal wall.”\n14- Would the author be able to clarify which study or facts supports the following claim : “lipids\ninterfere with cell dissociation and 10X single-cell partitioning and Gel Bead-in-Emulsion (GEM)\nformation?\"\nResponse:\nWe thank the reviewer for the request to clarify the statement regarding lipids. While we did not locate a\npublication that explicitly states “lipids interfere with GEM formation in the 10x workflow,” there is\nsupporting documentation from the 10x Genomics company indicating that high-lipid tissues (e.g.,\nadipose) present challenges in pelleting and single-cell suspension preparation (10x Genomics “Single\nCell Flex Tested Tissues” support page) and that non‑ideal suspension conditions (e.g., debris,\nnon‑singlet cells) may lead to microfluidic clogs or wetting failures in GEM generation (10x Genomics\nknowledge base). In our experience, incomplete dissociation or the presence of lipid aggregates can also\nlead to microfluidic channel clogging. To mitigate these issues, we included the delipidation step in the\nnuclei isolation protocol, which allowed reliable GEM formation and consistent single-nucleus capture.\n15- The authors might want to include a definition of endoreplication in their discussion.\nResponse:\nThe definition of endoreplication was included in the discussion section. It now reads: “Endoreplication\nis a type of cell cycle where there is DNA replication without cell division, leading to polyploid cells.”\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nResponse:\nThank you for the initiative to provide opportunity to early career researchers.\nReviewer #3 (Remarks to the Author)\nIn this article the authors present a well-executed set of experiments at single-nucleus resolution to\ncharacterise the complexity of the mosquito fat body—an organ central to both female reproductive\ndevelopment and innate immunity. The work is relatively descriptive in nature, as is often the case\nwith this approach, but the level of cellular and molecular complexity revealed is valuable and will\nprovide an important reference point for the field. It is therefore likely to be frequently cited.\nThe introduction is exemplary in its clarity. However, both I and a more junior colleague who helped\nme review the manuscript felt that some important detail was missing from the description of the\nexperimental set-up used to examine the effects of immune priming. This is a field in which the\nauthors have made many of the seminal contributions—indeed, they effectively “wrote the book” on\nthe subject—so the rationale deserves to be laid out more fully. The hypotheses underpinning these\nexperiments could be stated more explicitly, and fewer assumptions should be made about the\nreader’s familiarity with why particular time points, bacterial delivery methods, and bacteria–\nPlasmodium combinations were chosen.\nA similar point applies to aspects of the discussion concerning transcript localisation and\nendoduplication. The interpretations themselves are reasonable, but the arguments would be clearer\nwith more explicit rationale and fewer assumptions about prior knowledge. A little more empathy for\nthe reader in these explanations would substantially strengthen the manuscript.\nMore detailed comments and queries are provided below:\nIn the results section there needs to be an explanation of why they looked at the conditions they did.\nIt would be helpful to add a justification of why both single nucleus and bulk sequencing was used (i.e.\nwhat different results were expected from each one). Also on the experimental rationale, the\nmanuscript would be easier to understand if the authors add an explanation of why systemic bacterial\nchallenge by injection into the hemolymph is an important and relevant immune challenge to\nmosquito biology, including explaining the choice of bacteria used in the challenge (perhaps\nrepresentatives of gram positive/ negative?)\nResponse:\nThank you for the helpful suggestion to expand the explanation of our experimental design. We have\nrevised the results section to clarify the rationale behind the selected conditions and the use of both\nbulk and single-nucleus RNA sequencing.\nWe added a detailed explanation describing the complementary nature of bulk and snRNA-seq and why\nboth approaches were necessary for this study: “In this study, bulk and snRNA-seq were used to identify\nboth broad transcriptome changes, as well as cell-specific responses of Anopheles gambiae adult female\nfat body trophocytes to blood-feeding, bacterial challenge and immune priming (Figure 1A). Bulk RNA-\nseq was used to capture broad transcriptional changes with high depth coverage, allowing us to quantify\nglobal differences in gene expression across different physiological and immune conditions. In contrast,\nsnRNA-seq enabled the resolution of cell-type–specific transcriptional responses and the identification of\ndifferent trophocytes subpopulations. Using both methods allowed us to distinguish tissue-wide shifts\nfrom changes restricted to specific fat body cell types, providing a more comprehensive understanding of\nhow these stimuli reshape fat body biology.”\nWe expanded the description of the physiological and immune conditions analyzed in this study,\nexplaining their biological relevance to fat body function and mosquito immunity. The text in the revised\nmanuscript now reads: “Due to the importance of the fat body tissue on reproduction and immunity\nsamples were collected from the abdominal wall of healthy An. gambiae females fed on sugar or blood,\nas well as from females systemically challenged with bacteria or primed by infecting them with\nPlasmodium berghei (Figure 1A). Samples from females challenged systemically with a mixture of\nEscherichia coli and Micrococcus luteus were analyzed 4 hours post-injection, while the priming response\nwas evaluated six days after females were fed on P. berghei-infected mice. The response of fat body cells\nto blood feeding was investigated 24 hours after ingestion of a blood meal. The selected conditions\nrepresent the major physiological and immune challenges experienced by An. gambiae adult females:\nnutrient acquisition (sugar feeding), reproductive activation (blood feeding), acute antibacterial defense\n(systemic bacterial challenge), and long-term immune modulation (Plasmodium-mediated priming).\nTogether, these treatments capture a broad spectrum of stimuli that shape fat body function in nutrition\nand vector–pathogen interactions.”\nWe included additional details explaining the experimental design of the systemic infection and its\nimportance for understanding fat body immune responses, as follows: “The fat body plays an important\nrole in the mosquito systemic immune response by producing antimicrobial peptides and phenoloxidase\nenzymes that are secreted into the hemolymph and limit bacterial proliferation and mediate pathogen\nmelanization, respectively. However, cell type-specific responses remain poorly understood. The\ntranscriptional response of the abdominal body wall to bacterial infection was explored by challenging\nadult females with a systemic injection of a mixture of a Gram-negative (E. coli) and a Gram-positive\nbacteria (M. luteus), or a sterile PBS control, and harvesting the abdominal body wall 4 hours later. This\napproach ensured the direct delivery of a standardized bacterial load into the hemolymph, triggering a\nrobust and physiologically relevant immune response. The combination of Gram-positive and Gram-\nnegative bacteria provided a broad immune stimulus, ensuring the activation of the major immune\nsignaling pathways.”\nSeveral sections of the results presented interesting but incomplete stories. Adding either further\nexperiments or at least fuller interpretation of the observations with clearer recommendations for\nfuture work would greatly improve the clarity and interest of the manuscript to a wider audience.\nThese included:\n1. Does the trade-off between immunity and metabolism mentioned on p18 of the MS (lines 393-397)\nextend to measurable impacts on reproduction (as explored for Drosophila in Gupta et al 2022 DOI:\n10.1186/s12915-022-01328-w)? Importantly, how does bacterial or Plasmodium challenge impact the\nDNA replication or expression of yolk protein genes in trophocytes?\nResponse:\nThank you for citing Gupta et al. (2022, BMC Biology; DOI:10.1186/s12915-022-01328-w). We agree that\ntheir work is highly relevant to the trade-off concept we discuss. In their Drosophila study, they used\nsingle-nucleus sequencing to define subpopulations of fat body cells and showed that, following\nbacterial infection, all subpopulations mount immune responses. Crucially, in mated (reproductively\nactive) females, they saw ER stress and an impaired capacity to upregulate translation, including\nantimicrobial peptide synthesis, which they linked to reduced survival. By temporarily inhibiting\ntranslation before infection, they were able to restore immune function and survival, suggesting that\nprotein synthesis capacity is a major bottleneck when fat body must support both reproduction and\nimmunity.\nIn our study, we did not observe transcriptional changes in mRNA expression yolk protein genes or in\nDNA-replication–associated genes in response to bacterial challenge or Plasmodium priming under the\nexperimental conditions we used. However, it is possible that these challenges had an indirect effect on\nreproduction, since mosquitoes must allocate energetic resources between reproductive investment and\nimmune activation, and such physiological trade-offs may not necessarily manifest at the transcriptomic\nlevel under the conditions tested here.\nWe agree that future experiments—for example, measuring translation capacity, ER stress markers, or\nreproductive output (egg production) in challenged versus unchallenged mosquitoes—could test\nwhether a trade-off similar to that seen in Drosophila exist in Anopheles. We agree with the reviewer\nthat these are excellent questions, and it would be interesting to address them in future research, but\nthey are beyond the scope of the current manuscript.\n2. Vitellogenin mRNA localisation. A possible parallel with past observations in Aedes trophocytes\nentering the vitellogenic phase is mentioned (lines 412-420), with ribosomes relocating and plasma\nmembrane changes. Figure 6 shows a clear localized expression of Vg mRNA in the trophocytes at the\nhemolymph side using in situ hybridisation. However, given that the single cell results were based on\nsingle nuclei, one might expect that Vg mRNA would additionally be enriched in the nuclei of these\ncells compared to the other cell types, since transcripts outside of the nucleus should have been\nremoved during nucleus preparation and their data processing steps to remove ambient mRNA. In\nfigure 6 Vg mRNA cannot be seen in trophocyte nuclei. It should be explained why this this is. Is this\nbecause the level in the nuclei is low compared to the strong cytoplasmic accumulation, or is it due to\ncontamination of the sequenced nuclei with non-nuclear mRNA? Furthermore, there was a general\nissue with all in-situ microscopy presented that there was no marker for the cell membrane or sub\nmembrane cytoskeleton, making it impossible to interpret where the cell margins were. Adding a cell\noutlining marker would really aid interpretation of these images for the reader and make the cell\nspecific markers much more convincing.\nResponse:\nThe reviewer brings some interesting observations. Nuclear mRNA typically represents about 10% of\ncytoplasmic mRNA, although this proportion can vary across species and cell types. Additionally, certain\nmRNAs can be localized to specific subcellular regions, resulting in higher concentrations in those areas.\nWe believe this is the reason why all the probes for different genes had a strong cytoplasmic signal while\nthe nuclei signal could not be readily detected.\nBased on the bulk-RNA sequencing, which includes both the nuclei and cytoplasm, Vg mRNA expression\nincreases more than 4,000-fold in blood-fed mosquitoes, resulting in a very strong signal in the RNA FISH\nexperiments. The lack of detection of Vg RNA in the nuclei in in situ hybridizations is due to a much\nhigher concentration of mRNA in a very narrow region of the cytoplasm, on the side of the trophocytes\nfacing the hemolymph, and we need to reduce the potency of the laser to not saturate the signal coming\nfrom a very narrow area in the cytoplasm. We took extensive steps to clean the samples and applied\ndecontamination strategies to minimize ambient RNA contamination; however, a minor level of\ncontamination is possible.\nWe agree that the inclusion of a membrane or cytoskeleton marker would have improved interpretation\nof the RNA FISH images by providing clearer visualization of cell boundaries. We tested several\nmembrane dyes and cytoskeletal markers during protocol optimization; however, none were compatible\nwith our RNA FISH conditions, as they either interfered with probe hybridization or were lost during the\ndenaturation and wash steps. For this reason, we used BODIPY, which reliably stains neutral lipids and\nproved especially useful for distinguishing trophocytes from other fat body cell types, as their cytoplasm\nis filled with lipid vesicles.\n3. More explanation of how cells were counted is needed. A fuller explanation of the rationale for\ncounting cell types based on single nucleus sequencing and the error of these measurements (where\nreplicate experiments allow this to be estimated) should be added to the methods section. This is\nneeded to validate the many interesting observations on the changes in the proportions and numbers\nof cell typed during different treatments. For example, the story regarding circulating versus static\ngranulocytes. In the introduction, lines 61-67 mention that immune priming in An. gambiae is\nmediated by release of hemocyte differentiation factor when microbiota contacts the midgut\nepithelia, and that this triggers an increase in the proportion of circulating granulocytes. Theoretically\na changing proportion between circulating and fat-body associated granulocytes could be achieved\neither by release of fat body granulocytes to the haemolymph (resulting in fewer fat body\ngranulocytes but more circulating), or by cell division which would boost the circulating population\nleaving the fat-body associated population at the same size. These alternative hypotheses could have\nbeen investigated using their combined single nucleus followed by in situ hybridisation methods. In\ntheir investigation of the response of fat body associated granulocytes to P. berghei challenge there\nwas a clear transcriptional change but crucially, they state that they did not observe a significant\ndifference in the number of body wall-associated granulocytes (results lines 181-182). This leaves the\nreader wondering how they measured the number of granulocytes. Was this based on the relative\nproportion of nuclei with a granulocyte expression profile compared to other fat body cell types in the\nsingle nuclei sequencing? If this is the case, what is the quality control for this – how do we know that\nthe expression and physiological changes associated with the P. berghei challenge (or any of the other\ntreatments used in the manuscript) did not influence the recovery rate of nuclei from different cell\ntypes differently and bias the count? Can this observation be confirmed using HCR-FISH and counting\nfat body associated granulocytes based on cell-type specific markers in naïve vs challenged\nmosquitoes? Would the final interpretation of this result be that the boost to circulating granulocytes\nis not by release of fat body associated granulocytes? This issue of how cells of each type were\ncounted and how confident we can be in the counts/ relative proportions going on single nucleus\nclustering alone is also relevant for the stated observations about trophocytes (lines 263-265),\ngranulocytes following bacterial challenge (lines 356-360) and oenocytes (p369-371).\nResponse:\nWe appreciate the reviewer’s thoughtful comments regarding cell quantification and the interpretation\nof changes in cell proportions. We would like to clarify that we did not perform absolute cell counts in\nthis study. Our analysis relied on the number of nuclei recovered in each cluster in the snRNA-seq\ndataset, and these values were used only to describe the relative representation of transcriptional states\nrather than to infer absolute differences in cell numbers across conditions. We agree entirely that nuclei\nrecovery can be influenced by technical factors, including differential nuclear stability or accessibility\nacross cell types and treatments; therefore, we did not interpret shifts in cluster representation as\nquantitative biological counts. However, we think it is important to compare and document the relative\nabundance of different cell types between control and experimental samples that were processed side\nby side to prevent differences due to batch effects.\nOur goal in presenting these observations was to highlight transcriptional changes and raise biologically\nmeaningful hypotheses—for example, regarding the relationship between sessile and circulating\ngranulocytes—rather than to conclude mechanistically how these populations change in vivo. As the\nreviewer points out, distinguishing between granulocyte recruitment, release, or proliferation would\nrequire direct quantitative approaches such as FISH-based cell counting, imaging of labeled hemocytes,\nor lineage-tracking methods. Quantifying hemocytes embedded within the fat body is technically\nchallenging, and we did not attempt it here.\nWe have clarified in the methods section that the estimated cell proportions were based on the number\nof captured nuclei: ”The estimated relative cell abundance provided in Supplementary Table 2 represents\nthe relative proportion (%) of nuclei from each cluster relative to the total nuclei captured in each\nsample.”\nBased on the reviewer’s comments, we modified the text regarding the cell proportion changes between\nconditions:” Although we observed transcriptional differences between granulocytes from naïve and\nprimed females, we did not observe a difference in the proportion of captured nuclei from body wall-\nassociated granulocytes in response to immune priming.” and “We observed a doubling of the number\nof fat body-associated granulocytes following a bacterial challenge in snRNA-seq, suggesting either\nproliferation of resident cells and/or recruitment from circulation.”\nWe agree that the questions raised—particularly concerning the balance between circulating and fat-\nbody-associated granulocytes and the interpretation of trophocyte and oenocyte proportions—are\nimportant and remain open in the field. We are actively pursuing these issues in ongoing and future\nwork.\n4. The story regarding endoreplication in trophocytes was very interesting but seemed to finish before\nreaching a firm conclusion. They observed EdU-positive but pH3 negative trophocytes which would be\nconsistent with DNA replication without mitosis. Why did they not attempt to look at the ploidy in\nthese cells? This could be done using for example FACS or even just measuring the brightness of\nnucleic acid stains such as Hoechst using microscopy. Similarly, given that they applied a filter to\nremoved doublets from single nucleus data, can they be sure they did not accidentally filter out\ntrophocytes with a higher ploidy – how did the various QC metrics differ between potential doublets\nand endoreplicated nuclei?\nFurthermore, in this study they only investigated the response of the fat body to the first blood meal. If\nthe endoreplication occurs after the first blood feeding to boost transcription, this raises questions as to\nwhat happens to ploidy after 6 days when the transcriptional profile returns to pre-blood fed patterns,\nparticularly in their cluster “T1” – are all these extra DNA copies kept? Or perhaps could the “T1” cluster\n6 days after blood feeding be new cells and not the same T5 endoreplicated cells returning to pre-blood\nfed transcriptional patterns? If the cells in T1 are the T5 cells and ploidy remains at this boosted level,\ndoes that mean that after subsequent bloodmeals the transcriptional shift would be different since it\nshould no longer require genes involved in DNA replication since the endoreplication has already\nhappened?\nResponse:\nThank you for your questions and for suggesting experiments for future studies.\nWe performed the EdU and phospho-histone H3–positive (PH3) assays to determine if the drastic\nchanges in the number of cells in clusters T1 and T5 observed 24h after ingesting a blood meal could be\ndue to cell replication giving rise to a new cell population (T5). The EdU staining pattern in the blood-fed\nsamples showed that most trophocytes are EdU-positive, with some heterogeneity in the intensity, and\nnegative for PH3 staining. This indicates that most trophocyte from different subpopulations undergo\nsome level of DNA replication without cell division (endoreplication). These findings support the\nhypothesis that the T1 cluster undergoes a dramatic transcriptional change, with endoreplication and\nvery high expression of specific genes, such as Vg, giving rise to a very different transcriptional profile\nthat the software defines as a new (T5) cluster. This is a transient response, as most cells with the T5\ntranscriptional profile disappear when oogenesis is completed (6 days post-feeding) and return to the T1\ntranscriptional state.\nWe did not assess the ploidy of trophocytes in this study because a previous publication in Aedes aegypti\nhave already reported an increase in trophocyte ploidy following a blood meal [3]. We agree that it\nwould be very interesting to carry out in in vivo EdU staining and isolate nuclei and carry out FACS\nanalysis with both EdU and Dapi staining, to determine the level of ploidy and how it changes after the\nfirst or second blood meals. Those are interesting future experiments.\nWe analyze the distribution of the number of genes and UMIs in the nuclei from each sample\nindividually and use a software to identify potential doublets. However, no doublet nuclei were detected\nin any of the samples. Doublets are more problematic when working with dissociated tissues, as\nsometimes the dissociation is incomplete or when the membrane of two dissociated cells “stick” to each\nother. With the buffer conditions we used, we never observed nuclei doublets, in agreement with the\nbioinformatic analysis. Indeed, we observed that a substantial number of nuclei from the blood-fed\nsamples displayed higher numbers of detected features and UMIs compared to sugar-fed controls, but\nnone of those cells were removed from the analysis.\nWe agree that it would be interesting to examine the trophocyte ploidy at 6 days post-feeding, to\ndetermine if it persists and, if so, whether it would further increase after a second blood meal. If the\nincrease in ploidy persists, it may enhance vitellogenesis by supporting higher expression of yolk protein\ngenes. To our knowledge, no studies have reported how trophocyte ploidy changes following a second\nblood meal. We appreciate the interesting suggestions, but they are beyond the scope of this\nmanuscript.\n5. On the report of the result that hemocytes are the second most abundant cell type (lines 124-125)\n”Surprisingly, hemocytes (Hm1, Hm2) are the second most abundant cell type, representing 7.4% of\nthe cells (Supplementary Table 2)”, it needs to be explained why this is surprising?\nResponse:\nThank you for this comment. Most of the quantitation of hemocytes is based on circulating hemocytes.\nWe surprised, because although we have work with hemocytes for many years, we were not aware that\nthere were so many hemocytes associated with the fat body tissue. To be more objective, we have\nmodified the text, removing the observation that we are surprised, and now it reads: “Hemocytes (Hm1,\nHm2) are the second most abundant cell type, representing 7.4% of the cells (Supplementary Table 2).”\n6. The question of whether cells are “resident” or “associated” with the fat body does raise the\nquestion of whether they could move in and out. This is particularly relevant to the topic of the\nparallels between insect fat body and the vertebrate immune system (e.g. the potential role of T4\ntrophocytes in immune surveillance discussed lines 391-393), a topic that is of strong interest to the\ngeneral reader. They postulate in lines 356-357 “We observed a doubling of the number of fat body-associated\ngranulocytes in response to bacterial challenge, suggesting either proliferation of resident cells and/or recruitment from\ncirculation”. Although directly testing the alternative hypotheses of cell movement vs cell proliferation might be beyond the\nscope of this study, this question should be better highlighted in the discussion with a recommendation as to what sort of\nexperimental approach could definitively answer this question in future studies (for example tracking of cells over time).\nResponse:\nWe are very interested in exploring these aspects in future studies comparing sessile and circulating\nhemocytes. To our knowledge, there are currently no reports describing a hematopoietic organ in adult\nmosquitoes, which raises the intriguing question of how hemocyte populations are replenished. Our\ngroup previously identified a population of circulating, dividing granulocytes, and in the present study,\nwe also observed evidence of cell division (PH3–positive cells) among sessile hemocytes.\nThe mechanisms regulating the dynamics between tissue-associated (sessile) and freely circulating\nhemocytes remain poorly understood. To address these questions, we plan to perform single-cell RNA\nsequencing to compare sessile and circulating hemocytes in the same mosquitoes and investigate their\nresponse to different stimuli, including immune priming and bacterial infection, using a combination of\nIFA and RNA FISH microscopy.\nWe have modified the Discussion section to include a proposed experimental approach addressing these\npoints: “Future studies to explore the relative contribution of proliferation of fat body-associated\nhemocytes and recruitment of hemocytes circulating in the hemolymph will require longitudinal tracking\nand quantification of hemocyte populations in the same experimental samples, using a combination of\nsingle-cell RNA sequencing and high-resolution tissue imaging.”\nFurther discussion of how the Anopheles fat body is similar or different from other insects would have\nimproved the discussion section. This seems part of the stated aim of addressing a knowledge gap in\nAnopheline mosquitoes about the cellular composition and physiology of the fat body. The discussion\nsection alludes to studies of the fat body cellular composition in other organisms including Drosophila\nmelanogaster and Bombyx mori, and yet there is no discussion of the similarities and differences\nbetween what they observed in Anopheles gambiae fat body single nucleus sequencing and these\nearlier works.\nResponse:\nThank you for the opportunity to expand on the discussion of the comparative morphology of the fat\nbody between species and explain the differences between the previous studies on Drosophila, Bombyx\nmori, and Aedes aegypti. We added the following text to the discussion section, as suggested by the\nreviewer: “Drosophila melanogaster and Bombyx mori are non-hematophagous insects that feed\ncontinuously, and that fundamental dietary difference shapes fat body biology in ways that contrast with\nmosquitoes. In Drosophila, the fat body forms diffuse sheet-like layers with compact segmental oenocyte\nclusters [31], whereas B. mori possesses large, well-defined lobes and abundant oenocytes [32]. In\nmosquitoes such as Ae. aegypti and An. gambiae, however, the requirement of a blood meal for egg\ndevelopment drives drastic and transient transcriptional and physiological changes in fat body\ntrophocytes to sustain vitellogenesis [7, 12]. The mosquito fat body is organized into distinct lobes with\nmore dispersed oenocyte clusters [7]. In our study, we identified different trophocyte subpopulations,\nas well as oenocytes and other fat body-associated cell types, including hemocytes, epidermal cells and\nneuronal cells, in agreement with previous single cell studies in insects [28-30].”\nThere are a few technical considerations. As stated by my colleague above, the manuscript appears to\nbe generally technically sound, but with the rationale of experiments, some technical issues warranted\na clearer explanation. In the discussion the authors claim to have overcome technical barriers to\nnucleus isolation (line 330, 432). They give no details of these technical barriers and don’t show any\nquality control of the nuclei isolated prior to sequencing (for example microscopy to show nuclei\nmorphology). They seem to have followed initially a protocol similar to what was used in Gupta and\nLazzaro 2022 (https://doi.org/10.1080/19336934.2021.1978776) followed by using the Sigma “Nuclei\nPure Prep” standard protocol. Were other options for nuclei isolation tried and worked less well? As a\nminimum, images of the isolated nuclei for each treatment and yield as expressed as amount of\nnuclei/number of tissues dissected should be shown. It could also be acknowledged that Anopheles\noenocytes have been isolated in the past (Grigoraki et al 2020, 10.7554/eLife.58019), although this\nwas with the help of a transgenic GFP expressing line whereas the method developed in the current\nmanuscript are applicable to all genotypes.\nResponse:\nWe have submitted a detailed method manuscript to Bio-protocol where we provide the nuclei images\nand estimated yield of nuclei recovery, and it’s currently under review. With this protocol we were able\nto obtain high-quality nuclei, yielding approximately 106 nuclei from 25 body wall tissues. We added to\nthe discussion the technical challenges faced on isolating single cell from the fat body tissue that now\nreads:” Trophocytes are large cells with the cytoplasm filled with lipid droplets, making them fragile to\nhigh-quality single-cell isolation. Our optimized protocol involves a delipidation step, because high lipid\ncontent in the sample can lead to nuclei aggregation and interference with 10X single-cell partitioning\nand Gel Bead-in-Emulsion (GEM) formation. In our experience, we were unable to obtain high-quality\nintact fat body cells using enzymatic methods of tissue dissociation.”\nWe tested the protocol from Grigoraki I. 2020 that used trypsin to dissociate the tissue. In our\nexperience this enzymatic treatment was harsh on the trophocytes, leading to poor-quality isolated cells\nand RNA degradation and very few oenocytes were dissociated. Our goal was to characterize as many\ncell types associated with the fat body as possible. In our experience, oenocytes are much more resistant\nto the enzymatic treatments compared to trophocytes.\nThe number of biological replicates used for the single cell work was very low. Supplementary figure\n1G suggests 2* “naïve”, 2* “challenge”, and only one each of other conditions (although “control”\nmight be the same as one of them?). This low level of replication is likely due to the high cost of\nundertaking the single cell work but means that any important observations such as on the relative\nnumbers of cells in each cluster need to be confirmed by another method for statistical robustness.\nResponse:\nResponse: Thank you for the comment. Single-cell sequencing is usually carried out with a single sample,\nbecause the transcriptome from each cell is considered a replicate of how a tissue responds to a\nstimulus. We performed two biological replicates for the naïve and primed samples and one technical\nreplicate of the sugar-fed, blood-fed, PBS injected control and bacterial injected samples.\nAlthough we agree that proportion of captured cells are not the best method to estimate cell count and\nthat capturing bias might occur during library preparation, the proportion of cells in each cluster were\nsimilar between different conditions. We also processed the control and experimental samples for each\ntreatment side by side, on the same day to prevent any difference due to batch effects. We also carried\nout bulk-RNA sequencing in triplicates, which would reflect the transcriptional responses of the whole\ncell (nuclei and cytoplasm). The results of the snRNAseq are in agreement with the Bulk-sequencing\nanalysis.\nWe mentioned the cell differences as support information consistent with the observed transcriptional\nchanges, and highlighted only the dramatic shift in trophocyte populations, T1 to T5, between sugar-fed\nand blood-fed samples. The hypothesis raised regarding the shifts in cell populations such as\ngranulocytes are very interesting by outside the scope of this manuscript.\nOn the analysis of the single nucleus sequencing data, lines 102-103 state that the “cutoffs for the\nnumber of counts and features, as well as the percentage of mitochondrial and ribosomal content,\nwere set for each sample (Supplementary Figure 1)”. However, although supplementary figure 1 shows\nthe spread of the data it does not actually indicate what cutoffs were used or how the decision to set\nthese cutoffs was made. This should be made clear.\nResponse: Thank you for the suggestion. The cutoffs were described in the methods section: “Quality\ncontrol and cell filtering were performed using the Seurat R package (version 4.2.1). For each sample,\nnuclei were retained based on sample-specific thresholds for the number of detected genes (Features),\nthe percentage of mitochondrial gene expression, and the percentage of ribosomal gene expression, to\nremove low-quality nuclei and potential doublets. Cells were included if they expressed between 200\nand 2000 genes for most samples (Naïve 1, Naïve 2, Prime 1, Prime 2, and Blood-fed), with adjusted\nupper bounds for PBS-control, bacteria-injected (2500), and Sugar-fed samples (1500). To minimize the\ninclusion of damaged or stressed nuclei, cells with a mitochondria gene percentage exceeding 2.5%\n(Naïve 1 and Prime 1), 1.5% (Naïve 2 and Prime 2), 7% (PBS-control), 5% (Bacteria-injected), or 10%\n(Sugar-fed and Blood-fed) were excluded. Additionally, a ribosomal gene expression threshold was\napplied, with upper limits ranging from 10% to 25% across samples to account for potential variation in\nribosomal content between conditions.”\nThe selection of cluster specific markers seemed to have a very low bar in terms of the fold change\n(Avg log2FC > 0.5) – just 1.41 times more expression in the enriched clusters? The authors should\nexplain how this cut-off was decided on. In supplementary table 1 where these markers are described,\nthe proportion of cells with detectable expression was also rather low for some marker genes.\nResponse:\nThank you for your consideration about the cutoffs. The avg log2FC > 0.5 threshold (≈1.4-fold) is a\nstandard and widely used cutoff in single-cell RNA-seq analyses, including Seurat-based workflows. It is\nintentionally modest because single-nucleus data exhibit higher dropout rates and smaller dynamic\nrange than whole-cell scRNA-seq. Stronger thresholds (e.g., >1 or >1.5 log2FC) would eliminate many\nbiologically meaningful but sparsely detected nuclear transcripts. We therefore used a conservative fold-\nchange cutoff combined with specificity criteria (proportion of expressing cells, adjusted p-value, and\ndifferential expression compared to all other clusters) to ensure that markers reflected consistent\nenrichment despite nucleus-based technical dropout. The lower proportion of detectable expression for\nsome markers is expected for transcription factors and low-abundance genes in snRNA-seq datasets and\nis not unusual.\nLines 531-533 mention that “The gene annotation (gtf) file was filtered to retain protein-coding genes”.\nCould valuable data on the expression of non-protein coding genes such as long noncoding RNAs have\nbeen discarded at this point and how would its inclusion have affected the clustering?\nResponse: Thank you for your comment. Filtering the annotation file to retain protein-coding genes is a\ncommon step in snRNA-seq processing pipelines, particularly for species with incomplete or\ninconsistently annotated non-coding transcriptomes, such as Anopheles gambiae. While this approach\nremoves long noncoding RNAs (lncRNAs) and other non-protein-coding features, the impact on\nclustering is expected to be minimal for two reasons. First, most lncRNAs in mosquitoes remain poorly\nannotated, fragmented, or lacking validated transcript boundaries, which complicates quantification and\ncan introduce noise rather than meaningful signal. Second, clustering in single-nucleus datasets is\noverwhelmingly driven by the highly expressed and well-annotated protein-coding transcriptome; low-\nabundance or sparsely detected lncRNAs typically contribute little to the principal components that\ndefine cluster structure. Thus, while lncRNAs may have biological importance, their exclusion is unlikely\nto have altered the clustering results, and including poorly annotated non-coding features could have\nincreased technical noise without adding interpretable information. In a future project, we are planning\nto use the Bulk-RNA sequencing data to attempt to map all the reads to the genome and annotate new\ntranscripts, including lncRNAs, for these body wall samples. The assembly and annotation will be a time-\nconsuming project, but once completed, it will allow us to re-analyze our snRNA-seq data to investigate\nthe potential cell-specific expression and transcriptional responses of lnc-RNAs.\nFor the ambient RNA correction (lines 537-538 “Correction was performed using the decontX\nalgorithm from the celda package [48]” it should be specified if the same model was applied across all\nsamples or whether this was modelled individually in each sample.\nResponse: Thank you for the suggestion. The decontX model was applied to each sample before\nintegration. Methods section was modified to include this information: “Correction was performed in\neach sample using the decontX algorithm from the celda package [49], which models and removes\ncontamination from ambient background RNA in snRNA-seq data.”\nReviewer #4 (Remarks to the Author):\nde Carvalho and coworkers present a single nuclei RNAseq data set of Anopheles gambiae fat body.\nThey show a variety of cell types, and notably assign five trophocyte clusters. They show some cells\nresponding to bacterial challenge and, not surprisingly see a strong upregulation of vitellogenesis-\nrelated genes after a blood meal.\nThe introduction is beautifully written, the experiments are well described. The results show the\nexpected heterogeneity of cell types in this tissue.\nThis is a well-done study.\nResponse: Thank you for the positive feedback.\nMinor points:\n- a scheme of where the different trophocyte types are located within the abdomen would be helpful.\nResponse: Thank you for the suggestion. The trophocyte subpopulation did not show a particular\nlocalization within the abdominal body wall, and we were able to stain all trophocyte populations on the\nlateral, ventral and dorsal sides of the tissue.\n- could the tissue and subcellular locations of some mRNAs you see (especially Vg) be due to technical\nproblems of the in situ hybridization protocol? Just asking questions...\nResponse: The reviewer raises an important point. We had the same concern. We carefully evaluated\nthe possibility that the observed mRNA localization patterns could result from technical artifacts. The\nRNA FISH experiments were repeated multiple times, and the subcellular localization of vitellogenin (Vg)\nwas highly reproducible across independent dissections and hybridizations. Furthermore, the probes for\nseveral other genes hybridized in the same samples as the Vg probe and stained all the cytoplasm and\ndid not have the localized subcellular localization of Vg. This rules out the possibility of the lack of\nhomogeneous staining of the cytoplasm with the Vg probe was due to a lack of permeabilization. We\nalso obtained the same localization pattern using an alternative probe targeting a different gene that is\nalso highly induces by blood feeding. Taken together, these controls support the conclusion that the\nobserved Vg mRNA localization reflects a genuine biological distribution rather than technical artifacts.\n- a longer discussion of how the results of [30] compare to these findings would be appropriate.\nResponse: Thank you for the suggestion. We have expanded the discussion comparing our findings to\nprevious single cell papers: “Drosophila melanogaster and Bombyx mori are non-hematophagous insects\nthat feed continuously, and that fundamental dietary difference shapes fat body biology in ways that\ncontrast with mosquitoes. In Drosophila, the fat body forms diffuse sheet-like layers with compact\nsegmental oenocyte clusters [31], whereas B. mori possesses large, well-defined lobes and abundant\noenocytes [32]. In mosquitoes such as Ae. aegypti and An. gambiae, however, the requirement of a\nblood meal for egg development drives drastic and transient transcriptional and physiological changes in\nfat body trophocytes to sustain vitellogenesis [7, 12]. The mosquito fat body is organized into distinct\nlobes with more dispersed oenocyte clusters [7]. In our study, we identified different trophocyte\nsubpopulations, as well as oenocytes and other fat body-associated cell types, including hemocytes,\nepidermal cells and neuronal cells, in agreement with previous single cell studies in insects [28-30].”\nReferences\n1. Li, M., et al., Response of the mosquito immune system and symbiotic bacteria to\npathogen infection. Parasit Vectors, 2024. 17(1): p. 69.\n2. Sigle, L.T. and J.F. Hillyer, Mosquito hemocytes preferentially aggregate and\nphagocytose pathogens in the periostial regions of the heart that experience the\nmost hemolymph flow. Dev Comp Immunol, 2016. 55: p. 90–101.\n3. Dittmann, F., P.H. Kogan, and H.H. Hagedorn, Ploidy Levels and DNA-Synthesis\nin Fat-Body Cells of the Adult Mosquito, Aedes-Aegypti - the Role of Juvenile-\nHormone. Archives of Insect Biochemistry and Physiology, 1989. 12(3): p. 133–\n143."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all of our concerns comprehensively. They have conducted further analyses, generated new\nfigures and thoroughly revised the text. The manuscript is now much stronger, more rigorous and clearer than the original\nsubmission. The major concerns regarding infection validation and methodological aspects have been resolved with\ncompelling new data and arguments.\nBased on these revisions, the manuscript is ready for publication, and we congratulate authors for this great piece of work.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "The authors received extensive comments. I am happy with the changes they have made and detailed. Where these have\nnot been included I am happy with the rebuttal. I thank the authors for patiently engaging with the process. This will be an\nimportant reference work for the field.", "reviewer_4": "I feel this manuscript is ready for publication."}, "author_response": ""}]

Decision letter