Dynamic subcellular proteomics identifies regulators of adipocyte insulin action

Domain: chemistry
Published: 2026-02-28
Editorial outcome: major_revision

Abstract
Insulin acts on adipocytes to suppress lipolysis and increase glucose uptake to control whole-body glucose and lipid metabolism. Regulation of these processes by insulin signalling depends on changes in protein localisation. However, the extent of insulin-stimulated changes to the adipocyte spatial proteome, and the importance of these in the cellular insulin response, is unknown. Here, we use subcellular proteomics approaches to map acute insulin-stimulated protein relocalisation in adipocytes on a cell-wide scale. These data reveal extensive insulin-regulated protein redistribution, with hundreds of insulin-responsive proteins. These include the uncharacterised protein C3ORF18, which redistributes to the plasma membrane in response to insulin. Studies in C3ORF18-depleted adipocytes suggest this protein is required to maintain adipocyte insulin sensitivity. Overall, our data highlight the scale of protein relocalisation in the adipocyte insulin response, and provide an accessible resource to inform further studies into how changes in protein localisation contribute to cellular insulin responses.

Introduction
White adipose tissue is sensitive to hormonal and metabolic inputs that balance energy storage and energy release, coordinating adipocyte metabolism and organismal needs. Postprandially, increased circulating insulin promotes adipocyte energy storage by increasing glucose uptake and suppressing lipolysis. This metabolic response to insulin is mediated by an extensive signalling network1, which elicits cellular responses that require changes in protein subcellular localisation.

Insulin binding to its receptor, INSR, alters the subcellular distribution of specific proteins via several mechanisms. These include modulating protein-protein interactions via protein phosphorylation, recruiting proteins to membranes through lipid messenger generation (e.g., PI(3,4,5)P3), and through direct effects on membrane traffic. For example, recruitment of insulin receptor substrate 1 (IRS-1) and phosphoinositide 3-kinase (PI3K) to the activated INSR is mediated by binding to phospho-tyrosine residues on INSR and IRS1, respectively, whilst insulin-stimulated phosphorylation of the tuberous sclerosis complex (TSC)1/2 complex promotes its dissociation from the lysosomes2. Insulin signalling also directly targets known regulators of membrane trafficking (e.g., TBC1D4) to deliver glucose transporters (i.e., GLUT4) to the plasma membrane (PM) and promote glucose uptake3. Endosomal cargoes including transferrin receptor (TfR)4,5, insulin-like growth factor 2 receptor (IGF2R)/cation-independent mannose 6-phosphate receptor (CI-M6PR)6,7, and sodium/hydrogen exchanger 6 (NHE6)8 also undergo insulin-stimulated trafficking to the PM, although the physiological significance of endosomal cargo redistribution to the cell surface remains unclear. Collectively, these examples highlight how multiple mechanisms governing protein relocalisation play an essential role in the cellular insulin response. Moreover, insulin-stimulated protein redistribution is impaired in states of insulin resistance (e.g., GLUT4 delivery to the PM9), exemplifying the importance of regulating protein localisation in metabolic health.

Unbiased approaches to study the acute adipocyte insulin response have, to-date, focussed solely on protein phosphorylation1,10, while studies into insulin-regulated protein movement have been limited to studies on single proteins or identifying translocation to/from a single organelle8,11. Here, we combine unbiased global (Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC)12) and targeted PM profiling13 subcellular proteomics techniques to map the spatial proteome of 3T3-L1 adipocytes and identify changes in protein localisation in response to an acute insulin stimulus. These studies reveal >500 high-confidence insulin-responsive proteins. To demonstrate the utility of identifying proteins that move in response to insulin, we focus on C3ORF18, which redistributes to the PM following insulin stimulation. Studies in adipocytes depleted of C3ORF18 show that C3ORF18 plays a role in maintaining adipocyte insulin responsiveness. Overall, our work provides a key resource highlighting the dynamic adipocyte subcellular proteome and impetus for future mechanistic studies into how insulin regulates adipocyte function.

Results
Mapping the subcellular 3T3-L1 adipocyte proteome using LOPIT-DC
To map the 3T3-L1 adipocyte proteome, we modified the LOPIT-DC subcellular fractionation workflow12 to include an additional lipid droplet-enriched fraction (Fig. 1a). Cells were lysed and separated into 9 fractions by differential centrifugation, including a cytosol-enriched soluble fraction. To isolate lipid droplets for the tenth fraction, cells were lysed by nitrogen cavitation and floated through a sucrose gradient. Mass spectrometry analysis of the 10 fractions identified 4269 proteins in non-stimulated (basal) replicates (n = 3) and 4384 proteins in insulin-stimulated replicates (100 nM, 20 min; n = 3), with 4005 proteins identified across all replicates of both conditions (Fig. 1b). Data was processed as previously described12,14. A set of organelle marker proteins based on existing curated markers, proteins well established to reside in specific subcellular niches, were used to annotate datasets12,15,16 (total 531; Supplementary Data 1), and proteins were excluded as marker proteins if they appeared to relocalise in response to insulin. QSep17, which quantifies the separation of clusters in subcellular proteomics data, was used to confirm the separation of organelles in our annotated data (Supplementary Fig. 1a). The unique distribution of organelles across fractions was further confirmed visually through the profiles of the marker proteins (Fig. 1c, Supplementary Fig. 1b), and the formation of distinct clusters when visualised using linear dimensionality reduction (PCA) (Fig. 1d). In addition, the lipid droplet fraction was enriched for known lipid droplet-localised proteins including the perilipins (PLIN1, PLIN2, PLIN3, PLIN4), VPS13C, CIDEC, galectin-12, and ABHD5 (Supplementary Fig. 1c-d).

Protein localisation under each condition was predicted using the semi-supervised Bayesian machine learning algorithm Bayesian ANalysis of Differential Localisation Experiments (BANDLE)18. Accurate localisation assignment requires organelles to have ≥10 marker proteins in all replicates with a distinct distribution across fractions as training data18,19. These criteria excluded the use of both lipid droplets and endosomes as organelles for protein allocation using BANDLE. Of the 4005 proteins, 1755 proteins in the basal dataset and 1614 in the insulin-stimulated data were assigned to a single subcellular location with high-confidence (allocation probability >0.9; outlier probability < mean outlier probability for that organelle), in addition to the designated marker proteins (Fig. 1e; Supplementary Data 1). Of the allocated proteins, 1367 shared the same localisation under basal and insulin-stimulated conditions. The remaining ~50% of proteins not allocated to an organelle were classified as ‘undefined’. A majority of these are likely multi-localised proteins found in more than one subcellular domain, as previously reported8. Protein localisations in basal and insulin-stimulated adipocytes can be viewed interactively at https://proteome.shinyapps.io/adipocyte2025/.

Gene ontology cellular compartment (GoCC) enrichment analysis of proteins allocated to specific subcellular compartments showed enrichment of expected terms (Supplementary Data 1). Comparing our protein organelle assignments to localisations in cultured human adipocytes20 (Simpson–Golabi–Behmel syndrome (SGBS) adipocytes; Supplementary Data 1) revealed high concordance, particularly for proteins allocated to organelles typically well resolved by centrifugation approaches (i.e., mitochondria, cytosol) (>80%) (Supplementary Fig. 1e). Organelles that are harder to resolve, such as those comprising the secretory pathway (endoplasmic reticulum (ER)-Golgi-PM), had lower concordance, likely reflecting dynamic protein traffic and multi-localisation within this pathway. Accordingly, most proteins localised to the ER, Golgi or PM in 3T3-L1 adipocytes were assigned to organelles within the secretory pathway in SGBS adipocytes (Supplementary Fig. 1e).

Specific analysis of the lipid droplet fraction (fraction 10) revealed enrichment of all septin proteins identified (septin 2, 5, 6, 7, 8, 9, 10, 11) (Supplementary Fig. 1f) and these GTP–binding cytoskeletal proteins clustered near to lipid droplet marker proteins when visualised by PCA (Supplementary Fig. 1g). Septin 9 has been implicated in lipid droplet formation and localisation in non-adipocytes21,22, whilst septins 7 and 11 are reported to regulate adipocyte lipid storage and metabolism23–25. Immunostaining of septin 2 and 11 in mouse and human adipocytes revealed puncta close to lipid droplets in addition to more dispersed staining (Fig. 1f, Supplementary Fig. 1h). Further, knockdown of Septin2 during differentiation impaired lipid accumulation in the absence of changes in gene expression markers of differentiation (Fig. 1g, Supplementary Fig. 1i-k). Along with significant correlations between adipose tissue septin protein abundance and adipose-related clinical traits (Supplementary Fig. 1l)26–47, our data support the emerging role for this family of proteins in lipid handling/metabolism in adipocytes. Overall, we provide a high-resolution map of the 3T3-L1 adipocyte proteome which has high concordance to cultured human adipocytes and use these data to identify that septins localise to lipid droplets in mature adipocytes.

Insulin stimulates widespread protein relocalisation
Having mapped the adipocyte proteome under basal and acute insulin-stimulated conditions, we next identified proteins with altered localisation in response to insulin by performing differential localisation analysis using BANDLE18,48. This Bayesian analysis framework compares the distribution profiles of proteins in unstimulated (basal) and insulin-stimulated adipocytes, and is independent of whether a protein has been allocated to an organelle (as in Fig. 1e). We identified 899 out of 4005 proteins as candidate movers (differential localisation probability (diff. loc. prob.) > 0), of which 502 were high-confidence (diff. loc. prob. = 1) (Fig. 2a; Supplementary Data 1). Known insulin-responsive proteins including GLUT449 and LNPEP/IRAP50,51 were predicted to move with high-confidence (diff. loc. prob. = 1, Fig. 2b), validating our approach to detect insulin-responsive protein translocation. Changes in protein localisation were independent of IRAP and GLUT4 protein abundance (Supplementary Fig. 2a), and we have previously reported that total protein abundance is unchanged across the proteome in response to acute insulin stimulation10. Differentially localised proteins were enriched for signalling terms (Fig. 2c), including insulin signalling, and for proteins previously identified to contain insulin-regulated phosphosites in 3T3-L1 adipocytes (Fig. 2d, Supplementary Data 1). The known insulin-regulated phosphoproteins IRS-1 and 2 were predicted to move with high confidence (diff. loc. prob. = 1, Supplementary Fig. 2b), whilst several protein kinases, including MARK2, SIK2, and mTOR, relocalised and were phosphorylated in response to insulin (diff. loc. prob. = 1, 1, 0.14, Supplementary Fig. 2c). In addition, several regulators of mTOR complex 1 (mTORC1) activity were also differentially localised and targeted by insulin signalling. These included the well-characterised target of insulin signalling, TSC1/2 (diff. loc. prob. = 1, Supplementary Fig. 2d), as well as members of the GATOR1 (DEPDC5), GATOR2 (WDR59) and KICSTOR (SZT2) complexes which are typically associated with the amino acid sensing branch of mTORC1 regulation (diff. loc. prob. = 0.99, 0.1, 0.4 respectively; Fig. 2e). Notably, DEPDC5, WDR59 and SZT2 exhibited a profile shift consistent with dissociation from lysosomes in response to insulin (Fig. 2e). Together, these data suggest that protein phosphorylation and redistribution work in concert to mediate adipocyte insulin signalling, and highlight the utility of assessing both post-translational modification and protein localisation in studying mechanisms of cell signalling.

Membrane trafficking terms were also over-represented in differentially localised proteins (Fig. 2c, Supplementary Data 1). These included proteins implicated in GLUT411,52,53 (e.g., RALA, STX16, STX6, CDC42, PICALM, VTI1B, SCAMP1) and endosomal traffic (e.g., SNX12, VPS26A, SNX27, STX12) (Supplementary Data 1). We therefore assessed the localisation of cytosolic protein complexes that regulate cargo sorting through this system (e.g., retromer, ESCRT, AP-1, AP-2, AP-3, COPI, COPII, HOPS). These complexes had distinct distributions, and subunits within each complex clustered tightly when visualised by PCA (Fig. 2f, Supplementary Fig. 2e). Specific complexes had particularly striking relocalisation in response to insulin, including retromer (VPS26A/B-VPS29-VPS35, mean subunit diff. loc. prob. = 0.31; Fig. 2g) and AP-3 (mean subunit diff. loc. prob. = 0.77, Fig. 2g). However, others such as ESCRT-II and AP-1 did not alter their subcellular distribution (mean subunit diff. loc. prob. = 0, Fig. 2g). Together, these data suggest insulin signalling targets discrete components of the endolysosomal system.

The plasma membrane proteome undergoes dynamic remodelling in response to insulin
LOPIT-DC revealed the adipocyte PM proteome to be highly insulin responsive (Fig 3a, Supplementary Fig. 3a). To provide a more quantitative assessment of protein abundance changes at the PM in response to insulin, we used an orthologous cell surface biotinylation proteomics approach13 (Fig. 3b). Briefly, 3T3-L1 adipocytes were left untreated or stimulated with insulin (100 nM, 20 min) before surface proteins were biotinylated and isolated by affinity purification. Protein identification and relative cell surface abundance were determined by LC-MS. 342 proteins exhibited altered abundance at the PM following insulin stimulation (119 increased; 223 decreased; Fig. 3c, Supplementary Data 2). These data highlight a substantial remodelling of the adipocyte PM proteome in response to acute insulin stimulation. We identified known insulin-responsive PM proteins GLUT154, GLUT455–57, LNPEP/IRAP50,51, IGF2R/CI-M6PR6,7 and TfR4 which all increased in abundance at the PM, and INSR58, which decreased (Fig. 3c). Twenty-seven proteins that changed in abundance at the PM in response to insulin (p<0.05) were also predicted to differentially localise with high-confidence in our whole cell subcellular proteomics data (diff. loc. prob. = 1) (Supplementary Fig. 3b, Supplementary Data 2). Within these, C3ORF18, an uncharacterised protein of unknown function, had one of the greatest (1.8-fold) increases in abundance at the PM in response to insulin (Supplementary Data 2). Accordingly, in our LOPIT-DC data, C3ORF18 also relocalised in response to insulin (diff. loc. prob. = 1, Fig. 3d), with greater enrichment in fractions containing PM marker proteins after insulin stimulation (Fig. 3e).

C3ORF18 redistributes to the plasma membrane in response to insulin
The type III membrane protein C3ORF18 captured our attention as it underwent insulin-stimulated redistribution in both our whole-cell spatial proteomics and PM proteomic profiling, and was upregulated at mRNA and protein levels during adipocyte differentiation (Fig. 4a-b). Despite a predicted molecular weight of ~17 kDa, we detected an anti-C3ORF18 immunoreactive band at ~40 kDa following SDS-PAGE and immunoblot (Fig. 4a). We validated this ~40 kDa band as C3ORF18 by 1) observing the same apparent molecular weight with over-expressed epitope-tagged C3ORF18 (Supplementary Fig. 4a); 2) showing that siRNA-mediated depletion of C3ORF18 led to loss of this ~40 kDa band (Supplementary Fig. 4b); and 3) demonstrating this ~40 kDa band was insulin responsive in the PM fraction (Supplementary Fig. 4c). Additional biochemical studies revealed that the N-terminal luminal portion of C3ORF18 was responsible for its reduced electrophoretic mobility, perhaps because of high disorder in this region59 (Supplementary Fig. 4d-g).

To assess C3ORF18 subcellular localisation and validate the insulin-responsive relocalisation observed in our proteomics (Fig. 3c-e) and Western blot data (Supplementary Fig. 4c), we immunostained endogenous C3ORF18 in adipocytes under basal and insulin-stimulated conditions. This revealed C3ORF18 localised to both the perinuclear region and cell periphery, with increased peripheral staining in response to insulin (Fig. 4c-d). We observed similar localisation in adipocytes over-expressing HA-C3ORF18 (Supplementary Fig. 4h). HA-C3ORF18 did not colocalise with the ER-marker calnexin, and the perinuclear staining of HA-C3ORF18 was juxtaposed to the trans-Golgi marker TGN46 (Supplementary Fig. 4i), suggesting this is a post-Golgi compartment. HA-C3ORF18 had limited overlap with the early endosome marker EEA1 (Supplementary Fig. 4i), but endogenous C3ORF18 substantially colocalised with TfR and GLUT4, particularly in the perinuclear region (Fig. 4d). Quantitative analysis revealed greater colocalisation of C3ORF18 with TfR than GLUT4 (Fig. 4e), best exemplified when assessing C3ORF18 and TfR immunostaining in punctate structures towards the cell periphery under basal conditions (Fig. 4d). Importantly, this analysis also identified that C3ORF18 can be found in vesicles that are both TfR- and GLUT4-negative (Fig. 4d, inset). This suggests that C3ORF18 may reside, at least partially, in a distinct vesicle population to both TfR and GLUT4.

We next compared the C3ORF18 translocation responses to that of the known insulin-regulated proteins TfR and GLUT4. To more accurately quantify the insulin-responsive translocation of C3ORF18 to the PM, we used N-terminally HA-tagged C3ORF18 (HA-C3ORF18) expressing cell lines, where the HA-epitope is luminal/extracellular, to specifically label PM-localised C3ORF18. Surface staining in non-permeabilised cells confirmed insulin-responsive translocation of HA-C3ORF18 to the PM in 3T3-L1 adipocytes (Fig. 4f), SGBS adipocytes and L6 myotubes and myoblasts (Supplementary Fig. 4j). In addition, insulin-stimulation increased plasma membrane C3ORF18 in primary rat adipocytes (Supplementary Fig. 4k). As for GLUT4 and TfR, HA-C3ORF18 translocation was not unique to insulin, as 5 min EGF-stimulation also increased PM abundance (Supplementary Fig. 4l). Small molecule inhibition of PI3K (Wortmannin) and AKT (GDC-0068, MK-2206), but not MAPK (U0126), impaired insulin-stimulated HA-C3ORF18 PM translocation (Fig. 4g, Supplementary Fig. 4m), demonstrating C3ORF18 relocalisation is dependent on the PI3K-AKT branch of insulin signalling, as previously described for both GLUT4 and TfR (Supplementary Fig. 4n-o). Consistent with our co-localisation data (Fig. 4d-e), the dose response and kinetics of C3ORF18 translocation to the PM were more closely aligned with TfR than those of GLUT4 (Fig. 4h,i). For example, the t1/2 for C3ORF18 and TfR was <1 min and 1 min, respectively, but 6.2 min for GLUT4. Collectively these data suggest C3ORF18 redistributes to the PM from an insulin/growth factor-responsive, likely TfR-positive, endosomal compartment.

C3ORF18 is required for maximal insulin sensitivity
Having validated the insulin-responsive redistribution of C3ORF18 to the PM, we next determined whether this protein played a functional role in adipocyte biology. Although C3ORF18 was upregulated during differentiation (Fig. 4a-b), C3orf18 depletion from preadipocytes and during differentiation did not affect 3T3-L1 differentiation as measured by lipid accumulation or expression of adipocyte marker genes 8 d after initiation of differentiation (Supplementary Fig. 5a-d).

To study whether C3ORF18 plays a role in the adipocyte insulin response, we depleted C3orf18 in differentiated adipocytes (siRNA introduced 6 d after initiation of differentiation), with C3ORF18 protein abundance ~60% and ~90% of control cells after 4 (10 d after initiation of differentiation) and 8 d (14 d after initiation of differentiation) knockdown, respectively (Supplementary Fig. 5e). siRNA-mediated depletion of C3orf18 for 8 d reduced insulin-stimulated 2-deoxyglucose transport in human and mouse adipocytes (Fig. 5a-b, Supplementary Fig. 5f-g), and, consistent with this, impaired insulin-stimulated GLUT4 translocation to the PM (Fig. 5c). Impaired insulin responses were not specific to glucose transport, as insulin-stimulated TfR translocation to the cell surface was also reduced (Fig. 5d). To determine clinical relevance of these observations, we assessed the correlation between human adipose tissue C3ORF18 expression and clinical metabolic traits using the Adipose Tissue Knowledge Portal26. C3ORF18 expression was lower in subjects with higher circulating blood glucose and HbA1c (Fig. 5e), and increased with weight loss (Supplementary Fig. 5h). These clinical correlative data are directionally consistent with our data from cultured cells, indicating that C3ORF18 positively regulates insulin sensitivity in adipocytes.

Decreased insulin-stimulated 2-deoxyglucose transport after 8 d C3orf18 knockdown was likely driven by both lower protein abundance of the glucose transporters GLUT4 and GLUT1, independent of mRNA expression (Fig. 5f, Supplementary Fig. 5i), as well as reduced proximal insulin signalling (Fig. 5g, Supplementary Fig. 5j). Lower insulin signalling responses were independent of changes in INSR mRNA and protein abundance or localisation (Supplementary Fig. 5k-m), but were associated with decreased IRS1 protein, as well as reduced expression of adipocyte marker genes (Supplementary Fig. 5n-p).

To identify the earliest phenotypic responses to C3orf18 depletion from differentiated adipocytes, we assessed insulin responses after 4 d knockdown, when C3ORF18 protein levels were ~60% of control cells (Supplementary Fig. 5e). At this timepoint, insulin-stimulated 2-deoxyglucose, TfR translocation and TfR protein abundance were reduced in C3orf18 knockdown cells (Supplementary Fig. 5q-t). Insulin signalling and insulin-stimulated GLUT4 translocation were not affected (Supplementary Fig. 5u-v). These data suggest that endosomal recycling/protein stability is sensitive to C3ORF18 depletion, consistent with C3ORF18 subcellular localisation (Fig. 4d-e).

Methods
Ethics Statement
For studies using animals, ethical approval was granted by the University of Bath Animal Welfare and Ethical Review Body, and all procedures were conducted in accordance with UK Home Office regulations. All procedures were conducted in accordance with relevant guidelines and regulations.

Cell Culture
3T3-L1 fibroblast culture and differentiation into adipocytes
3T3-L1 fibroblasts obtained from David James (University of Sydney), originally from Howard Green (Harvard Medical School, Boston, MA), were maintained in Dulbecco’s modified Eagle’s medium (DMEM; Sigma) supplemented with 10% FBS (Gibco) and 2 mM GlutaMAX (Gibco) at 37°C with 10% CO2. Cells were passaged at ~60% confluence. Differentiation was induced at 100% confluence by the addition of 350 nM insulin, 0.5 mM 3-isobutyl-1-methylxanthine (IBMX), 250 nM dexamethasone and 400 nM biotin to the supplemented DMEM for 3 d, followed by 350 nM insulin for a further 3 d. Adipocytes were used 10-11 d following the initiation of differentiation.

SGBS preadipocyte culture and differentiation into adipocytes
SGBS adipocytes were provided by Professor Martin Wabitsch (Division of Pediatric Endocrinology and Diabetes, University Medical Center Ulm, Germany), and were cultured and differentiated according to the established protocol66,67. Briefly, SGBS fibroblasts were maintained in DMEM/F-12 (Sigma) supplemented with 10% FBS, 2 mM GlutaMAX, 33 μM biotin and 17 μM pantothenate. Differentiation was induced at 100% confluence by the addition of 0.01 mg/mL transferrin, 20 nM insulin, 100 nM cortisol, 0.2 nM T3, 25 nM dexamethasone, 250 μM IBMX, and 2 μM rosiglitazone to serum-free DMEM/F-12 for 4 d. Adipocytes were maintained in post differentiation media containing 0.01 mg/mL transferrin, 20 nM insulin, 100 nM cortisol and 0.2 nM T3 in serum-free DMEM/F-12 until use at day 12-14 after initiation of differentiation.

L6 myoblast culture and differentiation into myotubes
L6 myoblasts obtained from David James (University of Sydney) were maintained in AlphaMEM (Gibco) supplemented with 10% FBS and 2 mM GlutaMAX. Differentiation was induced at 80-90% confluence by culture in AlphaMEM supplemented with 2% horse serum (PAN-Biotech, P30-0702) and 2 mM GlutaMAX, and media changed every 2 days. Cells were used 5-7 days following onset of differentiation.

Generation of HA-C3ORF18 overexpressing stable cell lines
Empty vector or HA-C3ORF18-overexpressing 3T3-L1 and L6 cell lines were generated via retroviral transduction (pMMLV; VectorBuilder), as previously described68. Retrovirus was generated using the Plat-E packaging cell line69. HA-C3ORF18-overexpressing SGBS cells were generated via lentiviral transduction (pLV; VectorBuilder). Lentivirus was generated using the LentiX 293T packaging cell line (Takara Bio, 632180). Transduced cells were selected with 2 μg/mL puromycin and maintained and differentiated as described above. Puromycin was removed prior to cell differentiation. Plasmids are described in Supplementary Data 3.

HEK cell culture and transient HA-C3ORF18 overexpression
Wild type HEK-293 cells were cultured in high glucose DMEM supplemented with 10% FBS (Gibco) and 2 mM GlutaMAX (Gibco) at 37°C with 5% CO2. Cells were seeded at 3 x 105 cells/well into a 24-well plate 24 h prior to transfection with 0.5 μg DNA delivered using Lipofectimine2000 (Invitrogen). Cells were harvested for immunoblotting 24 h following transfection as described below. Plasmids are described in Supplementary Data 3.

LOPIT-DC
Subcellular fractionation
Differentiated 3T3-L1 adipocytes were cultured in low media volume as previously described for 48 h prior to the experiment (8 mL/10 cm dish, media changed every 24 h) to improve insulin sensitivity70. serum starved in basal DMEM (high glucose DMEM, GlutaMAX, 0.2% BSA) for 2 h and maintained in basal medium or stimulated with 100 nM insulin for 30 min. Four confluent 10 cm dishes were used per replicate per condition. Cells were washed twice with ice-cold PBS and harvested in homogenisation buffer (250 mM sucrose, 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) pH 7.4, 2 mM EDTA, 2 mM magnesium acetate tetrahydrate) containing EDTA-free phosphatase and protease inhibitor cocktail (Pierce). Cells were homogenised by 10 passes through a 21-gauge needle followed by 10 passes through a 27-gauge needle prior to centrifugation at 100 x g for 5 min at 4°C to remove unlysed cells. Cells were fractionated by centrifugation in a benchtop Beckman Allegra X-15R centrifuge (fraction 1-3), benchtop Eppendorf R5415R centrifuge (fraction 4-5), and a Beckman Optima LE-80K ultracentrifuge with a TLA-55 rotor (fraction 6-8) at 4°C (Supplementary Data 1) and pellets were kept on ice.

Pellets were resuspended in solubilisation buffer (8 M urea, 50 nM HEPES pH 7.4, 0.2% SDS) and sonicated using a tip probe sonicator. Supernatant from the last centrifugation step was retained as the cytosolic fraction, precipitated with ten volumes of cold acetone overnight at -20°C and centrifuged at 16,000 x g for 20 min at 4°C. Supernatant was removed and the pellet air dried for 15 min before resuspension in solubilisation buffer.

Lipid droplet isolation
Lipid droplets were isolated using a modified version of the protocol described by Brasaemle and Wolins71. 3T3-L1 adipocytes were serum-starved in basal DMEM for 2 h, and treated with 100 nM insulin for 30 min as described above. Six confluent 10 cm plates of adipocytes were used per condition. Cells were washed twice with ice cold PBS and harvested in Tris-EDTA buffer (20 mM Tris-HCl (pH 7.4), 1mM EDTA, cOmplete protease inhibitor (Merck)), and disrupted by nitrogen cavitation (Parr Instruments). Cells were exposed to 41 Barr of nitrogen for 20 min, after which the pressure was slowly released and the cell homogenate collected dropwise. Homogenate was spun at 1000 x g for 10 min at 4°C to remove unlysed cells, the supernatant made up in 20% sucrose (w/v) in Tris-EDTA buffer and overlaid with 5% and 0% sucrose in Tris-EDTA buffer in a 4 mL ultracentrifuge tube (Beckman Coulter). Samples were centrifuged at 28,000 x g for 30 min at 4°C with no brake in a Beckman Coulter Optima LE-80K ultracentrifuge using a SW 40 Ti rotor (Beckman Coulter). Lipid droplets were harvested from the top of the gradient, washed once and resuspended in Tris-EDTA buffer. Lipid droplet associated proteins were precipitated using chloroform-methanol and the protein pellet air dried, before being resuspended in 100 μL solubilisation buffer and incorporated into the LOPIT-DC workflow.

Proteomic sample preparation
100 μg of protein from each fraction was reduced with final concentration 10 mM DTT at 37°C for 1 h, then alkylated with final concentration 40 mM chloroacetamide (CAA) for 2 h at RT. Protein was precipitated with acetone overnight at -20°C, and pellets resuspended in HEPES, pH 8.5. Samples were digested with sequencing-grade trypsin (Promega) with a final enzyme:protein ratio of 1:40 for 1 h at 37°C, followed by a second trypsinisation step at 1:20 overnight at 37°C. Peptides were quantified using a fluorometric peptide assay (Thermo Fisher Scientific) according to manufacturer’s instructions.

50 μg of each fraction was labelled using a 10-plex tandem mass tag (TMT) kit (Thermo Fisher Scientific, Supplementary Data 1). TMT tags were equilibrated to RT and resuspended in 82 μL of LC-MS grade acetonitrile (ACN). 100 μL of each fraction (at concentration 0.5 μg/μL) was added to 41 μL of the appropriate tag. Labelling was allowed to proceed for 2 h on a shaker at RT. Reactions were quenched by adding 5% hydroxylamine and incubating for a further 45 min on a shaker at RT. The 10 fractions of each sample were then pooled into 1 LoBind Eppendorf and lyophilised using a SpeedyVac.

Samples were desalted using a C18 SepPak cartridge (100 mg sorbant, Waters). Cartridges were equilibrated with 2 washes of 100% ACN, 1 wash of 0.05% acetic acid and 3 washes of 0.1% trifluoroacetic acid (TFA). Samples were resuspended in TFA and the pH adjusted to < 3 before loading on column, and washed 3 times with 0.1% TFA, twice with 0.05% acetic acid, eluted in 70% ACN and 0.05% acetic acid and lyophilised.

Samples were pre-fractionated offline using reverse phase UPLC in an Acquity UPLC System with diode array detector (Waters) using an Acquity UPLC bridged ethyl hybrid C18 column (2.1-mm ID × 150-mm; 1.7-µm particle size; Waters). Peptide fractions were collected over a 50 min linear gradient, combined into 15 concatenated fractions, and lyophilised.

LC-MS3
MS analysis was performed using an Lumos Orbitrap mass spectrometer coupled to a Dionex Ultimate 3000 RSLC nanoUPLC system (Thermo Fisher Scientific, Waltham, MA, USA). Peptides were loaded onto a pre-column (Thermo Fisher Scientific PepMap 100 C18, 5 mm particle size, 100 Å pore size, 300 mm i.d. x 5 mm length) from the Ultimate 3000 auto-sampler with 0.1% formic acid (FA) for 3 min at a flow rate of 15 μL/min. After this period, the column valve was switched to allow elution of peptides from the pre-column onto the analytical column. Separation of peptides was performed by C18 reverse- phase chromatography at a flow rate of 300 nL/min using a Thermo Fisher Scientific reverse-phase nano Easy-spray column (Thermo Fisher Scientific PepMap C18, 2 mm particle size, 100 Å pore size, 75 μm i.d. x 50 cm length). Solvent A was water + 0.1% FA and solvent B was 80% ACN, 20% water + 0.1% FA. The linear gradient employed was 2-40% B in 93 min (total LC run time was 120 min including a high organic wash step and column re-equilibration).

The eluted peptides from the C18 column LC eluant were sprayed into the mass spectrometer by means of an Easy-Spray source (Thermo Fisher Scientific). All m/z values of eluting peptide ions were measured in an Orbitrap mass analyser, set at a resolution of 120,000 and were scanned between m/z 380-1500 Da. Data dependent MS/MS scans (Top Speed) were employed to automatically isolate and fragment precursor ions by collision-induced dissociation (CID; normalised collision energy (NCE): 35%) which were analysed in the linear ion trap. Singly charged ions and ions with unassigned charge states were excluded from being selected for MS/MS and a dynamic exclusion window of 70 s was employed. The top 10 most abundant fragment ions from each MS/MS event were then selected for a further stage of fragmentation by synchronous precursor selection (SPS) MS3 in the HCD high energy collision cell using HCD (NCE: 65%). The m/z values and relative abundances of each reporter ion and all fragments (mass range from 100-500 Da) in each MS3 step were measured in the Orbitrap analyser, which was set at a resolution of 50,000. This was performed in cycles of 10 MS3 events before the Lumos instrument reverted to scanning the m/z ratios of the intact peptide ions and the cycle continued.

MS spectra processing and peptide and protein identification
Raw LC-MS data files were processed with Proteome Discoverer v2.5 (Thermo Fisher Scientific) using the Mascot server 2.8.3 (Matrix Science). The MS data was run against a Swiss-Prot Mus musculus database (downloaded September 2019) and the common repository of adventitious proteins (cRAP, v1.0). Precursor and fragment mass tolerances were set to 10 ppm and 0.2 Da, respectively. Trypsin was set as the enzyme of choice and a maximum of 2 missed cleavages were allowed. Fixed modifications were set to carbamidomethyl(C) TMT6plex (N-term) and TMT6plex (K), and variable modifications were set to carbamyl(N-term), carbamyl(K), carbamyl(R), deamidation(N,Q), oxidation (M) and TMT6plex (S/T). Percolator version 3.05 was used to assess the false discovery rate (FDR) and only high-confidence peptides were retained. Quantification of the MS3 reporter ions was performed within the Proteome Discoverer workflow using the Most Confident Centroid method for peak integration and integration tolerance of 20 p.p.m. Reporter ion intensities were adjusted to the batch-specific TMT reporter ion isotope distributions prior to processing (Supplementary Data 1). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD061017.

Spatial proteomics analysis
All proteomics data analysis was performed in R (v4.3.2) using the Bioconductor packages pRoloc (v1.42.0)19, bandle (v1.6)18,48, clusterProfiler (v4.10.1)72,73 and ggplot2 (v3.5.1). Peptide spectrum match (PSM)-level quantitation output from Proteome Discoverer was imported into R and non-specific filtering was performed as previously described12,74 to retain high quality PSMs. PSMs were sum normalised and aggregated to protein level intensities using the ‘robust’ method75 within the pRoloc R package76. Replicates were concatenated to yield 4005 proteins quantified across all 6 replicates and allow the generation of a single subcellular map for each condition. The data was annotated with marker proteins, i.e. proteins well established to localise to a specific subcellular compartment, from previous subcellular proteomics experiments12,15,77. Additional marker proteins were identified through examining protein localisation in UniProt for proteins reported only to be localised to one organelle, and by mining the literature. Proteins were manually excluded as marker proteins if they appeared to relocalise in response to insulin. Under de Duve’s principle78, proteins residing in the same subcellular compartment are expected to share characteristic fractionation profiles. BANDLE leverages this by comparing the profiles of proteins with unknown localisation to those of curated organelle markers to infer subcellular assignments.

To assess the resolution and the quality of separation between subcellular niches in the data, a QSep score was computed using the pRoloc package in R17. QSep calculates the Euclidean distance between raw protein profiles for each set of organelle markers, with higher values indicating better separation in a multi-dimensional space. Dimensionality reduction using principal component analysis was also performed to visually assess subcellular separation indicated by the presence of distinct clusters. The final marker list incorporated 531 proteins from 9 subcellular niches; cytosol, ER, Golgi apparatus, lysosomes, mitochondrion, nucleus, peroxisomes, PM, and ribosomes (Supplementary Data 1).

The Bayesian ANalysis of Differential Localisation Experiments (BANDLE) algorithm from the bandle package48 was used to assign proteins to subcellular niches and predict differential localisation with the following parameters; 30,000 MCMC iterations, 5,000 burn-in iterations, 4 chains, and 1/20 thinning. Each protein was assigned to a subcellular niche for which it had the highest mean allocation probability, as described in the bandle vignette on Bioconductor (https://bioconductor.org/packages/release/bioc/html/bandle.html)79. Allocations were thresholded to retain only high confidence allocations. Proteins were assigned to a subcellular niche if their allocation probability was >0.9 and their outlier probability was < median outlier probability for that organelle. Proteins were deemed differentially localised if differential localisation probability > 0. Proteins with differential localisation probability = 1 were considered ‘high confidence’ and all others as ‘candidates’.

Enrichment analysis
Gene ontology80 cellular compartment enrichment was performed in turn for proteins assigned to each organelle (not including marker proteins) using clusterProfiler72 . Gene set enrichment analysis was performed using Gene ontology biological process and KEGG81 enrichment in clusterProfiler, and REACTOME82 enrichment analysis was performed using reactomePA (v1.46). In all analyses p-values were adjusted using the Benjamini-Hochberg procedure83.

Phosphoproteomics enrichment analysis
Proteins in Humphrey (2013)1 with phosphorylation sites with -/+ 2 fold change in response to insulin at any time point (0.25, 0.5, 1, 2, 5, 10, 20, 60 min) or proteins from Fazakerley (2023)10 with phosphorylation sites significantly up- or down-regulated in response to insulin were considered “insulin-regulated”. This list was converted to mouse uniprot IDs using UniProt align, with only reviewed entries taken. Enrichment of insulin-stimulated phosphorylation in the proteins predicted to differentially localise vs. not was determined using a Fisher’s exact test in R. Insulin-stimulated phosphorylation status of all proteins identified in LOPIT-DC in both phosphorylation data sets are shown in Supplementary Data 1.

Plasma membrane proteomics
Plasma membrane proteomics was performed as previously described13 with minor modifications. Differentiated 3T3-L1 adipocytes were cultured in low media volume as described above for 48h prior to the experiment, serum starved in basal DMEM for 2 h and maintained in basal medium or stimulated with 100 nM insulin for 30 min. Three confluent 10 cm dishes were used per replicate per condition, and six biological replicates were performed. Cells were washed twice with ice-cold PBS containing MgCl2 and CaCl2 (Sigma) and all subsequent steps were performed on ice. An oxidation/biotinylation mix comprising 200 mM aminooxybiotin (Biotium), 3 mM sodium meta-periodate (Thermo Fisher Scientific) and 10 mM aniline (Sigma) in ice-cold PBS pH 6.7 was added and dishes were rocked on ice for 1 h at 4°C in the dark. The reaction was quenched by adding glycerol to a final concentration of 1 mM for 5 min. Cells were washed twice with PBS pH 7.4 and lysed in a lysis buffer comprising 1.6% Triton X-100, 10 mM Tris-HCl pH 7.6, 150 mM NaCl, 5 mM iodoacetamide (IAA) and cOmplete protease inhibitor (Merck) for 30 min on ice. Nuclei (pellet) and lipid (top phase) were removed by centrifugation at 4°C, 13,000 x g for 10 min, which was repeated three times. 5 mg of protein was incubated with high-affinity streptavidin agarose beads (Pierce, 20357, 10 μL beads/mg protein) at 4°C for 18 h on a rotor to capture biotinylated glycoproteins. Beads were washed extensively with lysis buffer and PBS/0.5% SDS, using Poly-Prep columns (BioRad) attached to a vacuum manifold. Captured proteins were reduced with PBS/0.5% SDS/100 mM DTT for 20 min at RT, washed extensively with UC buffer (6 M urea in 0.1 M Tris-HCl pH 7.6), alkylated with 50 mM IAA (Sigma) for 20 min at RT, and digested on-bead with trypsin (Promega) in 200 mM HEPES pH 8.5 for 3 h at 37°C. Tryptic peptides were collected and stored at -80°C until TMT labelling.

Peptide labelling with tandem mass tags
TMT reagents (TMTpro 16-plex, 0.8 mg, Thermo Fisher Scientific) were dissolved in 43 μL anhydrous ACN and 10 μL added to peptide at a final ACN concentration of 30% (v/v). Samples were labelled as in Supplementary Data 2, incubated at RT for 1 h, and reaction quenched with hydroxylamine to a final concentration of 0.5% (v/v). TMT-labelled samples were combined at a 1:1:1:1:1:1:1:1:1:1:1:1 ratio. The sample was vacuum-centrifuged to near dryness and subjected to C18-based solid phase extraction (Sep-Pak, Waters). An unfractionated ‘single shot’ was analysed initially to ensure similar peptide loading across each TMT channel, thus avoiding the need for excessive electronic normalization. As all normalization factors were >0.5 and <2, data for each single-shot experiment was analysed with data for the corresponding fractions to increase the overall number of peptides quantified. Normalization is discussed in ‘data analysis’ and high pH reversed-phase (HpRP) fractionation is discussed below.

Offline HpRP fractionation
TMT-labelled tryptic peptides were subjected to HpRP fractionation using an Ultimate 3000 RSLC UHPLC system (Thermo Fisher Scientific) equipped with a 2.1 mm internal diameter (ID) x 25 cm long, 1.7 μLm particle Kinetix Evo C18 column (Phenomenex). Mobile phase consisted of A: 3% ACN, B: 100 % ACN and C: 200 mM ammonium formate pH 10. Isocratic conditions were 90% A/10% C, and C was maintained at 10% throughout the gradient elution. Separations were conducted at 45°C. Samples were loaded at 200 μL/min for 5 min. The flow rate was then increased to 400 μL/min over 5 min, after which the gradient elution proceeded as follows: 0-19% B over 10 min, 19-34% B over 14.25 min, 34-50% B over 8.75 min, followed by a 10 min wash at 90% B. UV absorbance was monitored at 280 nm and 15 s fractions were collected into 96 well microplates using the integrated fraction collector. Adjacent columns of fractions were combined, resulting in six combined fractions. Wells were excluded prior to the start or after the cessation of elution of peptide-rich fractions, as identified from the UV trace. Fractions were dried in a vacuum centrifuge and resuspended in 10 μL MS solvent (4% ACN/5% FA) prior to LC-MS3.

LC-MS3
Labelled samples were analysed using an Orbitrap Fusion Lumos (Thermo Fisher Scientific). An Ultimate 3000 RSLC UHPLC machine equipped with a 300 μm internal diameter × 5 mm Acclaim PepMap μ-Precolumn (Thermo Fisher Scientific) and a 75 μm internal dimeter × 50 cm 2.1 μm particle Acclaim PepMap RSLC analytical column were used. The loading solvent was 0.1% FA. The analytical solvent consisted of 0.1% FA (A) and 80% ACN + 0.1% FA (B). All separations were carried out at 40°C. Samples were loaded at 5 μL/min for 5 min in loading solvent. For the single-shot sample, the analytical gradient consisted of 3-7% B over 3 min, 7-37% B over 173 min, followed by a 4 min wash at 95% B and equilibration at 3% B for 15 min. For fractionated samples, the analytical gradient consisted of 3-7% B over 4 min, 7-37% B over 116 min, followed by a 4 min wash at 95% B and equilibration at 3% B for 15 min. Each analysis used a MultiNotch MS3-based TMT method 84,85. The following settings were used: MS1: 380-1500 Th, 120,000 resolution, 2×105 automatic gain control (AGC) target, 50 ms maximum injection time. MS2: quadrupole isolation at an isolation width of mass-to-charge ratio (m/z) 0.7, collision-induced dissociation fragmentation (normalized collision energy (NCE) 34) with ion trap scanning in turbo mode from m/z 120, 1.5x104 AGC target, 120 ms maximum injection time. MS3: in Synchronous Precursor Selection mode, the top 10 MS2 ions were selected for HCD fragmentation (NCE 45) and scanned in the Orbitrap at 60,000 resolution with an AGC target of 1×105 and a maximum accumulation time of 150 ms. Ions were not accumulated for all parallelisable time. The entire MS/MS/MS cycle had a target time of 3 s. Dynamic exclusion was set to ± 10 ppm for 70 s. MS2 fragmentation was triggered on precursors 5×103 counts and above. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD061616.

Data analysis
Mass spectra were processed using a Sequest-based software pipeline for quantitative proteomics, ‘MassPike’, through a collaborative arrangement with Professor Steven Gygi’s laboratory at Harvard Medical School. MS spectra were converted to mzXML using an extractor built upon Thermo Fisher’s RAW File Reader library (version 4.0.26). In this extractor, the standard mzxml format has been augmented with additional custom fields that are specific to ion trap and Orbitrap mass spectrometry and essential for TMT quantitation. These additional fields include ion injection times for each scan, Fourier Transform-derived baseline and noise values calculated for every Orbitrap scan, isolation widths for each scan type, scan event numbers, and elapsed scan times. This software is a component of the MassPike software platform and is licensed by Harvard Medical School.

A combined database was constructed from the mouse Uniprot database (2019) and common contaminants such as porcine trypsin and endoproteinase LysC. The combined database was concatenated with a reverse database composed of all protein sequences in reversed order. Searches were performed using a 20-ppm precursor ion tolerance. Fragment ion tolerance was set to 1.0 Th. TMT tags on lysine residues and peptide N termini (304.2071 Da) and carbamidomethylation of cysteine residues (57.02146 Da) were set as static modifications, while oxidation of methionine residues (15.99492 Da) was set as a variable modification.

To control the fraction of erroneous protein identifications, a target-decoy strategy was employed86. Peptide spectral matches (PSMs) were filtered to an initial peptide-level FDR of 1% with subsequent filtering to attain a final protein-level FDR of 1%. PSM filtering was performed using a linear discriminant analysis, as described previously86. This distinguishes correct from incorrect peptide IDs in a manner analogous to the widely used Percolator algorithm (https://noble.gs.washington.edu/proj/percolator/), through employing a distinct machine learning algorithm. The following parameters were considered: XCorr, ∆Cn, missed cleavages, peptide length, charge state, and precursor mass accuracy. Protein assembly was guided by principles of parsimony to produce the smallest set of proteins necessary to account for all observed peptides (algorithm described in86).

Proteins were quantified by summing TMT reporter ion counts across all matching peptide-spectral matches using ‘MassPike’, as described previously85. Briefly, a 0.003 Th window around the theoretical m/z of each reporter ion was scanned for ions and the maximum intensity nearest to the theoretical m/z was used. The primary determinant of quantitation quality is the number of TMT reporter ions detected in each MS3 spectrum, which is directly proportional to the signal-to-noise (S:N) ratio observed for each ion. An isolation specificity filter with a cut-off of 50% was additionally employed to minimize peptide co-isolation85. Peptide-spectral matches with poor quality MS3 spectra (a combined S:N ratio of less than 250 across all TMT reporter ions) or no MS3 spectra at all were excluded from quantitation. Peptides meeting the stated criteria for reliable quantitation were then summed by parent protein, in effect weighting the contributions of individual peptides to the total protein signal based on their individual TMT reporter ion yields. Protein quantitation values were exported for further analysis in Excel.

For protein quantitation, reverse and contaminant proteins were removed, then each reporter ion channel was summed across all quantified proteins and normalised assuming equal protein loading across all channels. For further analysis and display in Figures, fractional TMT signals were used (i.e. reporting the fraction of maximal signal observed for each protein in each TMT channel, rather than the absolute normalised signal intensity). This effectively corrected for differences in the numbers of peptides observed per protein. P values were calculated using the method of significance A and corrected for multiple hypothesis testing in Perseus version 1.5.2.20.487.

Immunoblot analysis of isolated PM fraction
Following incubation with high-affinity streptavidin agarose beads as described above, beads were washed with lysis buffer, PBS/0.5% SDS and UC buffer, and incubated with 4x Laemmeli Sample buffer (Bio-Rad) and tris(2-carboxyethyl)phosphine powder (TCEP; Thermo Fisher Scientific) at 95°C to elute proteins for immunoblot analysis.

Immunoblotting
Adipocytes were cultured in reduced media volumes as previously described for 48 h prior to the experiment (250 μL media per well of 24-well plate, media changed every 24 h) to improve insulin sensitivity70. Cells were washed twice in ice-cold PBS and lysed in RIPA lysis buffer (50 nM Tris pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% Triton, 0.5% Na Deoxycholate, 0.1% SDS, 1% glycerol) containing EDTA-free Phosphatase and Protease Inhibitor Cocktail (Pierce). Cell lysates were sonicated and centrifuged at 16,000 x g, 20 min, 4°C to remove lipid (top layer) and unlysed cells/debris (pellet). Protein concentration of the infranatant was quantified using a BCA Protein Assay Kit (Pierce) according to manufacturer’s instructions. Lysates were denatured by mixing with 4x Laemmeli Sample buffer, reduced with TCEP and heated at 65°C for 10 min. Protein was resolved by SDS-PAGE using 4-20% (or 10% for C3ORF18) Mini-PROTEAN TGX Stain-Free Gels (Bio-Rad) and transferred to a nitrocellulose membrane using the Trans- Blot Turbo Transfer System (Bio-Rad). Membranes were blocked in 5% (w/v) dried skimmed milk (Marvel) in tris-buffered saline for 1 h at RT, followed by overnight incubation at 4°C with the appropriate primary antibody (Supplementary Data 3). Membranes were incubated with the appropriate horseradish peroxidase (HRP) or fluorophore-conjugated secondary antibodies (Supplementary Data 3) diluted 1:5000 in 5% (w/v) dried skimmed milk for 1 h at RT. Signals were detected using enhanced chemiluminescence (ECL; Thermo Fisher Scientific) or in the appropriate fluorescence channel on a ChemiDoc MP Imaging System (Bio-Rad).

Band intensities were quantified using ImageLab (v6.1, Bio-Rad). Data in Fig. 5g and Supplementary Fig. 5j and 5v were min-max normalised within replicates to account for varying signal total intensity between replicates.

PNGase F-mediated deglycosylation of N-linked glycans
Cells were lysed in RIPA buffer, and protein concentration determined using a BCA assay. Proteins were reduced using 50 mM DTT, heated at 65 °C for 10 min and cooled on ice, and 1 µL/20µg protein PNGase F (Promega) added. Samples were incubated at 37 °C for 2 h before Laemmli buffer was added and samples resolved by SDS-PAGE.

O-linked deglycosylation
O-linked deglycosylation was performed using the GlycoProfile™ β-Elimination Kit (Sigma, PP0540) according to the manufacturer’s instructions. Briefly, glycoprotein samples were incubated with the β-elimination reagent mixture under alkaline conditions to chemically remove O-linked glycans, followed by neutralization and cleanup using centrifugal filtration units. Deglycosylated protein samples were subsequently prepared for SDS-PAGE analysis.

Isolation and subfractionation of primary rat adipocytes
This study used archived subcellular membrane fractions from epididymal fat pad tissue harvested from (n=4) male Wistar rats (180 – 200g) which were humanely killed using a UK Home Office Schedule 1 method. Ethical approval was granted by the University of Bath Animal Welfare and Ethical Review Body, and all animal procedures were conducted in accordance with UK Home Office regulations. Animals were housed under conditions of 12 h light:12 h darkness, a temperature of 21 ± 2 °C and relative humidity of 55 ± 10%. Standard chow diet and water were available ad libitum. For the purpose of preparing primary adipocytes from epididymal fat pads, only male rats could be used. Primary adipocytes were prepared from the whole epididymal fat pads according to the method previously described88 with some modifications. The epididymal fat tissue was quickly removed and rinsed in 2.5% (w/v) BSA / Krebs-Ringers-HEPES buffer (KRH) (140 mM NaCl, 4.7 mM KCl, 2.5 mM CaCl2, 1.25 mM MgCl2, 2.5 mM NaH2PO4, 10 mM HEPES, pH 7.4) containing 200 nM adenosine (through the procedure the buffer is kept at 37°C). The washed tissue was placed in KRH buffer (1 g of tissue / 2 mL) containing 1 mg/mL collagenase (Worthington, Type I), 3.5% (w/v) albumin, 5 mM glucose, and 200 nM adenosine and minced finely with scissors. The tissue suspension was shaken at 100 rpm in a shaking water bath at 37°C for approximately 20 min until most of the tissue lumps were digested. The resulting cell suspension was filtered through a nylon mesh (250 m mesh size, Lockertex), returned to 37°C, fresh 2.5% (w/v) BSA/KRH buffer containing 200nM adenosine added, and the cells were allowed to float. The infranatant buffer was removed using a blunt needle (2 mm dia. x 100 mm) attached to a 20 mL plastic syringe, and 15-20 mL 2.5% (w/v) BSA/KRH buffer (with 200nM adenosine) was added. The cells were gently resuspended and then allowed to float. This washing procedure was repeated 3 times. The cell suspension was adjusted to a cytocrit of 20% approximately, (using a capillary tube which was centrifuged at 1500 g for 30 s, and expressed as the ratio of the length of the packed cell fraction in the tube to the total length of the suspension in the tube). The cells are then ready to be used. Insulin stimulation was performed with 20 nM insulin for 20 min. At the end of the stimulation, cells were washed once with KRH (no BSA supplementation) and once in HES buffer (20 mM HEAPES, 1 mM EDTA, 250 mM sucrose, pH 7.4) supplemented with protease and phosphatase inhibitors and kept at 18°C and homogenized with 10 strokes of a glass/Teflon Potter-Elvehjem homogenizer (Thomas Scientific, USA) before proceeding with the subcellular fractionation protocol.

Subcellular fractionation was performed by differential centrifugation in OptimaMax Ultracentrifuge according to the method of Simpson et al, (1983)89. Briefly, homogenate was centrifuged at 1000 g for 1 min at 4°C to separate fat from cellular fractions. The tubes were left on ice solidify the fat layer. The homogenate was aspirated using a syringe attached to a 12G hypodermic needle and the pellet and fat layer on top were discarded. The homogenate was then spun at 17000 g for 20 min at 4°C (TLA100.3 rotor). The pellet from this spin was resuspended and loaded on a sucrose cushion (20 mM HEPES, 1 mM EDTA, 1.12 M sucrose, pH 7.4) and spun at 104000 g for 20 min at 4°C (TLS55 rotor). The plasma membrane fraction was collected from the interface and spun twice at 76000 g for 9 min at 4°C (TLA 100.3 rotor) to remove remaining sucrose and contaminating endosomal fractions and cytosol. Purified plasma membranes were resuspended in 100 mM Tris pH 8.0 + 2% SDS.

siRNA knockdown in differentiated adipocytes
siRNA knockdown in 3T3-L1 adipocytes was performed as previously described90,91. Briefly, siRNA targeting mouse C3ORF18 (6430571L13Rik, siGENOME SMARTpool #235599; Dharmacon Horizon) at final concentration 50 nM was delivered to differentiated adipocytes (day 6 following differentiation) using TransIT-X2 (Mirus Bio). Cells were fed with fresh media 24 h following transfection, and transfected again 96 h after the initial transfection as previously described91. Functional assays were performed 96 h following the second transfection.

SGBS adipocytes were transfected as described above, with minor modifications. Forward transfection with Lipofectamine RNAiMax (Thermo Fisher Scientific) was used to deliver siGENOME SMART pool #51161 (human C3ORF18) at final concentration 50 nM on day 10 and day 14 after differentiation. Functional assays were performed 96 h following the second transfection.

siRNA knockdown during differentiation
3T3-L1 fibroblasts were seeded on matrigel coated 96 well Pheno plates (Revvity- 6055302) and grown for 3 to 4 days until 100% confluency. To maintain silencing of target genes throughout differentiation, forward transfection of siRNA was performed on days –2, 0, 3 and 6 of differentiation. Briefly, siRNA at final concentration 25 nM was delivered using Lipofectamine RNAimax (1:33, Thermo-13778075) diluted in OptiMEM. Mixture was incubated at room temperature for 20 min before added to cells in 3T3-L1 growth media described above. For Septin 2 knockdown, siGENOME SMARTpool #18 000 was used.

qPCR
RNA extractions were performed using the RNeasy Mini kit (Qiagen 1152 #74104). Concentrations of RNA samples were quantified using NanoDrop. cDNA synthesis from 500 ng RNA was performed using the GoScript Reverse Transcriptase kit (Promega #A2801). Real-time (RT)-polymerase chain reaction (PCR) was performed using TaqMan or SYBR Green Master Mix on an ABI QuantStudio 5. Primers are described in Supplementary Data 3.

Glucose transport
Glucose transport assays were performed as previously described70. Adipocytes were cultured in reduced media volumes as previously described for 48 h prior to the experiment (125 μl media per well of 48-well plate, media changed every 24 h) to improve insulin sensitivity70. Cells were serum-starved for 2 h in DMEM containing 0.2% BSA at 37°C, 10% CO2, washed and incubated in pre-warmed Krebs–Ringer phosphate (KRP) buffer containing 0.2% bovine serum albumin (KRP buffer; 0.6 mM Na2HPO4, 0.4 mM NaH2PO4, 120 mM NaCl, 6 mM KCl, 1 mM CaCl2, 1.2 mM MgSO4 and 12.5 mM HEPES (pH 7.4)) for 10 min, and then stimulated with 0.5 or 100 nM insulin for 20 min. To determine non-specific glucose uptake, 25 μM cytochalasin B (in ethanol, Sigma Aldrich) was added to control wells before addition of 2-[3H]deoxyglucose (2-DG) (PerkinElmer). During the final 5 min, 2-DG (0.25 μCi, 50 μM) was added to cells to measure steady-state rates of 2-DG uptake. Cells were then moved to ice, washed with ice-cold PBS, and solubilised in PBS containing 1% (v/v) Triton X-100. Tracer uptake was quantified by liquid scintillation counting on the TriCarb 2900TR (PerkinElmer) and data normalised for protein content.

Immunofluorescence
Cell surface immunofluorescence plate assays (HA-C3ORF18/GLUT4/TFR)
PM translocation of HA-C3ORF18, GLUT4 and TfR in 3T3-L1 adipocytes was assessed as previously described60,70, with minor modifications. Adipocytes were cultured in reduced media volumes as previously described for 48 h prior to the experiment (50 μl media per well of 96-well plate, media changed every 24 h) to increase insulin sensitivity70 . Cells were serum starved for 2 h and stimulated with insulin at doses and for times indicated. For assessment of PM translocation following EGF treatment, cells were serum starved for 2h and treated with mouse EGF (Gibco, PMG8041) for 5 minutes at doses indicated. Where inhibitors were used, cells were pre-treated with 200 nM Wortmannin (Sigma, W1628), 10 μM GDC-0068 (Selleck Chemicals S2808), MK2206 (MedChemExpress, HY-10358) or U0126 (Cell guidance systems, S106) for 15 min prior to insulin stimulation. Cells were washed by gently immersing the 96-well plates 10 times in beakers containing ice-cold PBS (five washes in each of two 1 L beakers containing PBS; all subsequent PBS washes were performed using this method). Plates were placed on ice and residual PBS was removed with a multichannel pipette. Cells were fixed with 4% paraformaldehyde (PFA) for 5 min on ice and 10 min at room temperature. PFA was quenched with 50 mM glycine in PBS for 10 min. Cells were blocked with 5% normal swine serum (NSS; Dako, X0901) in PBS for 30 min. Cells were then incubated with primary antibody solution (Supplementary Data 3) in 2% NSS in PBS for 1 h at room temperature, washed in PBS, and incubated with secondary antibody solution in 2% NSS in PBS for 1 h at room temperature in the dark. Cells were stored in PBS containing 2.5% DABCO, 10% glycerol and pH 8.5 as well as sealed and kept at 4 °C in the dark before imaging. Plates were equilibrated to room temperature for 30 min before imaging.

Plates were imaged on the Perkin Elmer Opera Phenix High Content Screening System and mid-section confocal images were obtained using a x20 water objective (NA 1.0), with 2-pixel binning. Excitation wavelengths and emission filters used were as follows: surface HA: 488 nm, 500–550 nm; endogenous surface TfR: 568 nm, 570-630; endogenous surface GLUT4: 647 nm, 650-760; and Hoechst: 405 nm, 435–480 nm. The microscope settings were automated and predefined before imaging the entire plate, meaning identical positions in each well were sampled across all wells and conditions without any human supervision. Nine positions towards the centre of each well were selected for imaging. Following the acquisition, positions across each plate and well were inspected at random to ensure proper seeding/staining for quality assurance. Mean fluorescence intensity was calculated to determine surface amount of protein.

Surface translocation of HA-C3ORF18 in L6 myoblasts and myotubes was performed as described above, with plates imaged using a x40 water objective with 40-50 fields of view per well used to quantify mean fluorescence intensity.

Surface translocation of HA-C3ORF18 in SGBS adipocytes was performed as described above with minor modifications. Cells serum starved for 4 h, stimulated, washed with PBS and incubated with primary antibody on ice for 2 h. Cells were washed in PBS, fixed with 4% PFA for 5 min on ice and 10 min at room temperature and secondary antibodies added as described above. Plates were imaged using a x40 water objective and 18 fields of view used to quantify mean fluorescence intensity in each well.

Colocalisation immunofluorescence (Opera Phenix)
Immunofluorescence to assess colocalisation of C3ORF18, GLUT4 and TfR was performed as described above with 0.1% saponin added to the blocking, primary and secondary antibody solutions to permeabilise cells. Plates were imaged on the Perkin Elmer Opera Phenix High Content Screening System and mid-section confocal images were obtained using a ×63 water objective. Colocalisation Threshold plugin in ImageJ Fiji v2.9.0 was used to compute Mander’s coefficient.

Immunofluorescence (SP8)
SEPTIN-2 and SEPTIN-11:
Differentiated 3T3-L1 adipocytes were reseeded onto glass coverslips, fixed with 4% PFA in PHEM buffer (60 mM PIPES, 25 mM HEPES, 10 mM EGTA, 10 mM MgCl2 pH 6.8), permeabilised with 0.1% saponin in PHEM buffer and blocked with 5 % NSS in PBS. Samples were incubated with primary antibodies (Supplementary Data 3) in 2% NSS, 0.1% saponin in PBS for 1 h at room temperature. Coverslips were washed with 0.1% saponin in PBS and incubated with secondary antibodies and BODIPYTM 493/503 (1:1000, Invitrogen, D3922) in 2% NSS in PBS for 1 h at room temperature. Imaging was performed using a Leica SP8 microscope with a x63 Plan-Apochromat oil objective. Image acquisition was performed using Leica Application Software (Leica, v3.5.7.2) and images noise reduced using the median module with 2 iterations.

C3ORF18:
Images acquired on Leica STP8 (Fig. 4d and Supplementary Fig. 4i) were further deconvoluted using the Deconvolution Express module in Huygens Professional 25.10 using default settings. Confocal planes identified to be having the best focus is used for representation in the manuscript.

Quantitative assessment of lipid droplet accumulation during adipogenesis
Longitudinal quantification of lipid droplet accumulation during adipogenesis was performed with the Digital Phase Contrast (DPC) imaging module of Perkin Elmer Opera Phoenix High Content Screening system. Object thresholding and mean intensity analysis measurements were performed using Harmony 5.2 analysis software. On day 8, samples were fixed and stained with Hoechst, to determine total cell number.

Statistical analysis
All statistical analyses were performed in Graphpad Prism 10 unless otherwise stated. Two-tailed paired Student’s tests were used to compare the means between 2 groups. One/two-way ANOVA with Šidák correction for multiple comparisons was used for multigroup comparisons. A mixed-effects model was used for multiple comparisons in Supplementary Fig. 5g and Supplementary Fig. 5j and v. Variations among replicates were expected to have normal distributions and equal variance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Conway et al report an analysis of the proteome of adipocytes incubated with and without insulin, and a qualitative analysis of the changes in the adipocyte plasma membrane proteome in response to insulin. Overall, this work is well performed, interesting and will, as the authors note, be a useful resource for the field. However, on balance I found this work to be quite descriptive in places, I have some concerns with the data analysis and overall found this to be a limited mechanistic advancement. Specific issues: Of the proteins which redistribute in response to insulin, how many of these are specific for insulin? This is an important question which is not addressed. It is known that (for example) endosomal trafficking and cell signaling can be influenced by a wide array of growth factors and receptors, so a difficulty I have with this data is the assertion that the proteins which move are targets of the insulin cascade. I appreciate this is a huge ask, and would not suggest that the authors need to do experimental work to address this, but this places a rather large caveat over the data and its interpretation, which the authors do not address. This could be addressed by some of the comments on Figure 4. Figure 4a is not convincing. Figure 4b should display controls and comparison with GLUT4. The data in figure 4c relate to my point above. The effect of insulin is small, ~70%. This is of a magnitude observed for endosomal proteins, like the transferrin receptor. Is this insulin-specific? This could be an example whereby the authors address the issues noted above. Figure 5. I would like to see the authors also present this data so that 5a-5d are normalized to the control (0 insulin) for each data set. The knockdown data are on average lower for all parameters. These are not statistically significant, but a lower basal value means that the fold increase in 2-DG, GLUT4 levels, etc., may not be as significantly different as the absolute values show. When analyzed this way, is there a difference between control and knockdown? There may well be, but this should be presented. Are GLUT4 levels changed when C3ORF18 is knocked-down? Figure 1c reveals that the plasma membrane is enriched in fraction 8, with little evidence of markers in fraction 7 (pink line in 1c). Why does insulin stimulation not enrich GLUT4 and IRAP in fraction 8 (figure 2b)? Minor issues: Figure 2D is not discussed in the text. This reviewer did not understand what should be taken from Figure 3A; could you improve the legend?", "reviewer_2": "This manuscript entitled “Dynamic subcellular proteomics identifies novel regulators of adipocyte insulin action” presents a comprehensive study of insulin-regulated protein redistribution in 3T3-L1 adipocytes using advanced subcellular proteomics approaches, including LOPIT-DC and surface biotinylation. The findings are exciting and likely to be of broad interest. The authors provide a detailed map of the subcellular localization of the adipocyte proteome, where many proteins align well with their expected localizations. The strength of the approach is further supported by the observation of known insulin-induced protein relocalizations and phosphorylation events. Notably, the identification of a novel candidate regulator, C3ORF18, as an insulin-responsive protein is intriguing and may open new avenues for research in insulin signaling and trafficking. I commend the authors for their efforts to validate the identity of C3ORF18, a protein with a predicted molecular weight (MW) of ~17 kDa, observed as a ~40 kDa species. However, Figure 4a is not entirely convincing. While there is an increase in signal intensity upon insulin stimulation, the smear-like appearance of the signal lacks a clear, discrete band, which complicates interpretation. Given that C3ORF18 is barely detectable under basal conditions (as shown in Figure 4a and Supplementary Figure 4c), it is unclear how the authors conclude that it undergoes subcellular relocalization. Furthermore, there appears to be an inconsistency between Figures 4a and 4b: C3ORF18 is not expressed in 3T3-L1 cells at day 0 in Figure 4a, yet it is detected in siControl cells in Figure 4b. Could the authors clarify this discrepancy? Is there a corresponding change in C3ORF18 MS intensity between basal and insulin-stimulated conditions? Additionally, the nature of the apparent MW shift observed for C3ORF18 should be addressed. What post-translational modifications (e.g., glycosylation) could account for this substantial shift on SDS-PAGE? It would be useful to treat the samples with glycosidases to determine whether the migration pattern changes, supporting the presence of glycosylation or other modifications. To strengthen Figure 4b, I suggest including DAPI staining to help assess whether C3ORF18 localizes to the nucleus or perinuclear region. In the current images, the distinction is unclear, and co-staining would provide valuable spatial context. Moreover, in basal conditions, the apparent association of C3ORF18 with ribosomes (Figure 3d) suggests a possible localization to the rough ER, which aligns with its predicted membrane-associated nature. Co-localization experiments with ER markers, or proximity ligation assays (PLA), would help confirm this interpretation. The observed relocalization of C3ORF18 following inhibition of PI3K and AKT appears robust; however, a representative image would help the reader better appreciate the morphological differences under these conditions. As an additional analysis, I recommend exploring C3ORF18 dependency using large-scale CRISPR screen datasets, filtered for adipocyte or metabolic cell types. This could offer insights into functional interactions or pathways related to C3ORF18. Lastly, the term \"PM\" (plasma membrane) is used in the abstract without being defined. Please define it upon first use. This study presents valuable insights into the spatial dynamics of the adipocyte proteome in response to insulin. While the findings regarding C3ORF18 are compelling, several points require clarification or additional validation to solidify its proposed role.", "reviewer_3": "In their study, Conway et al. use LOPIT, a subcellular proteomics approach, in a 3T3-L1 mouse cell model to screen for protein relocalizations following insulin stimulation. They identify C3ORF18, a protein of unknown function, as a potential novel player in insulin signaling. The manuscript is clearly structured, and the data are clearly presented and accessible via a Shiny app. The approach is interesting and allows for the unbiased identification of novel components involved in signaling. However, the biological relevance of the study is somewhat limited, and the overall outcome modest. The new insights gained are relatively minor, and the follow-up analysis of C3ORF18 is very superficial. The function of the protein remains unclear, and its role in insulin signaling is not convincingly established. In addition, the information about the used methods in the text itself is too short and difficult to follow, lacking the necessary detail to fully understand the data. Overall, while the technical approach is of interest, the study would benefit from a more thorough characterization of C3ORF18 and clearer methodological documentation. As it stands, the manuscript presents only limited biological insight. Specific points: • Please provide a clearer explanation in the main text of how the LOPIT approach works, including the principle of fractionation, and how BANDLE assigns proteins to compartments based on distribution profiles. It is currently unclear how marker sets are chosen and whether they are assumed to remain static between conditions. • The number of markers used appears large, and it seems they are not reassigned under insulin stimulation. Does the method assume these markers are static and do not relocalize? If so, this is a potential limitation. • While some organelle profiles (e.g., mitochondria) are clearly separated, others, such as plasma membrane and Golgi, appear overlapping and are harder to distinguish. This is also reflected in their lower concordance with published data. Please provide a quantitative metric or quality score for the compartment assignments to support interpretability. • Line 111: If proteins in the ER–Golgi–PM axis show low concordance with other datasets, how can changes in localization be confidently attributed to insulin signaling rather than inherent trafficking variability? • From ~4000 detected proteins, only ~1700 were assigned to compartments. Please explain why the remaining proteins could not be assigned. What thresholds or criteria (e.g., probability cutoffs) were used and how was it set? Are proteins with lower assignment confidence also more likely to show differential localization? • Please describe in more detail in the text what the second proteomics method is. Can you compare the changes in the PM detected via LOPIT-DC and biotinylating? What is different and why? Why was it necessary in addition to LOPIT? What new information does it provide that LOPIT does not? • What happens to lipid droplets in the P1–P8 fractions? Could LD-associated proteins be counted more than once due to their presence in both the LD fraction and earlier fractions? • Septins are discussed in the context of lipid droplets. Do any septins relocalize in response to insulin? What is their possible role in adipocyte biology and lipid storage? • Interpretation of ER accumulation upon insulin: Some proteins show ER accumulation upon insulin treatment. How do you interpret this? Could this indicate a block or re-routing in vesicular trafficking rather than a classical translocation? • Protein abundance vs. localization change (Line 138): For IRAP, the differential localization probability is high, but the shift appears to reflect abundance rather than spatial redistribution. How does your model distinguish these scenarios? • Line 146: What is the exact diff. loc. prob. threshold used to define significant relocalization? For proteins like mTOR, the localization shift is not obvious from the profiles—please clarify. • Line 153: When proteins like DEPDC5 or SZT2 are said to dissociate from lysosomes, where do they relocalize? Is this to the cytosol or another compartment? • Line 195: Has C3ORF18 been identified in other proteomic studies of adipocyte differentiation? If so, please cite relevant references. • Figures 4b and 4e: The nuclear localization of C3ORF18 is suggested, but no nuclear marker is shown. Likewise, endosomal markers should be used to support the claim that the protein localizes to insulin-responsive endosomes. • Line 242: The effect of C3ORF18 knockdown on AKT signaling is claimed, but no clear quantification is shown, and the Western blot quality is suboptimal. Please provide quantitative data and clearer blots. • Clarify C3ORF18 relocalization dynamics: The current description is ambiguous. Does C3ORF18 move from the nucleus, endosomes, or another compartment to the plasma membrane? How do endosomes fit into the proposed mechanism? • Predicted interactors of C3ORF18: Many predicted interactors are mitochondrial proteins. How does this reconcile with its observed localization and proposed function in insulin signaling? • Line 248: The statement that C3ORF18 improves insulin sensitivity in adipose tissue is not supported by the presented data. This conclusion should be supported with additional experiments. • The functional role of C3ORF18 doesn’t become clear. Is its relocalization upon insulin treatment dependent on phosphorylation? Is its role primarily in signaling or trafficking? Based on your data, how might C3ORF18 mechanistically influence the insulin signaling cascade? A speculative model or hypothesis would strengthen the discussion. Minor comments: · Line 43: phrasing should be improved. · Line 123 and 126: Extended data Fig.1h is mentioned before Fig.1g. · Please change colours of PM markers (basal vs insulin) in Fig. 3e so those can be distinguished by the reader. · Any idea on what the band at 80-90kDa is in the C3ORF18 blots? · Line 204: there is no figure 3f."}, "author_response": "We thank all reviewers for their time and feedback on our manuscript. In response, we have substantially revised our manuscript, most notably: 1. Provided greater detail and clarity surrounding data analysis and interpretation of the subcellular proteomics (LOPIT) data. 2. Performed higher resolution imaging of C3ORF18 localisation. These new data provide more clarity of C3ORF18 subcellular localisation and suggest that C3ORF18 resides, at least partially, in a distinct vesicular compartment to GLUT4 and TfR. 3. Provided a more detailed biochemical assessment of C3ORF18, including a possible explanation for its higher apparent molecular weight on SDS-PAGE. 4. Included data on C3ORF18 translocation in primary adipocytes. 5. Included additional data on how loss of C3ORF18 impairs adipocyte responses through altered protein abundance of insulin signalling intermediates and glucose transporters. We have also included more minor additions, including: 1. Additional imaging of Septin localisation in human adipocytes. 2. New analysis of the effect of Sept2 knockdown on adipocyte differentiation. 3. Assessment of C3ORF18 translocation in response to an alternate growth factor, EGF, showing that this protein is not uniquely responsive to insulin. We hope you and the Reviewers agree that these changes have substantially improved the accessibility, scope and impact of our manuscript. Please see point-by-point responses to individual reviewer comments below (in blue), with specific reference to changes to the manuscript test in red. Reviewer 1 Conway et al report an analysis of the proteome of adipocytes incubated with and without insulin, and a qualitative analysis of the changes in the adipocyte plasma membrane proteome in response to insulin. Overall, this work is well performed, interesting and will, as the authors note, be a useful resource for the field. However, on balance I found this work to be quite descriptive in places, I have some concerns with the data analysis and overall found this to be a limited mechanistic advancement. We have addressed concerns around data analysis in detail below and edited the manuscript accordingly. We acknowledge that there are some more descriptive sections within the manuscript, but suggest that these are necessary to some extent when introducing a new large data set. We have now added additional data that provides more mechanistic insight, described below. Specific issues: Of the proteins which redistribute in response to insulin, how many of these are specific for insulin? This is an important question which is not addressed. It is known that (for example) endosomal trafficking and cell signaling can be influenced by a wide array of growth factors and receptors, so a difficulty I have with this data is the assertion that the proteins which move are targets of the insulin cascade. I appreciate this is a huge ask, and would not suggest that the authors need to do experimental work to address this, but this places a rather large caveat over the data and its interpretation, which the authors do not address. This could be addressed by some of the comments on Figure 4. Our studies have focused on protein translocation in response to insulin in adipocytes We have not specifically studied whether proteins we found to redistribute with insulin also moved with alternate growth factors. Many growth factor RTKs utilise similar intracellular signalling machinery (e.g., PI3K/AKT, MAPK), so it is entirely possible that insulin responsive proteins are responsive to alternate growth factors. However, and as stated by the Reviewer, undertaking a comprehensive proteomics profiling of adipocytes/different cell types in response to multiple growth factors is beyond the scope of this study. We have performed experiments in our adipocyte model to, at least partially, address this question. 3T3-L1 adipocytes express EGFR and increase glucose uptake in response to EGF (PMID: 7917789) (Reviewer Figure 1A). As with insulin, EGF-stimulation increased phosphorylation of AKT and MAPK (Reviewer Figure 1B), albeit with more transient kinetics than insulin. EGF-mediated AKT phosphorylation was highest at 5 min, but returned towards baseline by 15 min (Reviewer Figure 1B). With this in mind, we assessed whether 5 min EGF stimulation caused plasma membrane translocation of GLUT4, TfR and C3ORF18 (New Extended Data Figure 4l). GLUT4, TfR and C3ORF18 increased plasma membrane abundance in response to 5 min EGF stimulation, suggesting that C3ORF18 (and indeed GLUT4 and TfR) translocation is likely driven by AKT activation and is not specific to insulin signalling in adipocytes. See Page 6: As for GLUT4 and TfR, HA-C3ORF18 translocation was not unique to insulin, as 5 min EGF-stimulation also increased PM abundance (Extended Data Fig. 4l). Reviewer Figure 1: a) Glucose uptake in 3T3-L1 adipocytes following 5 min (left) or 15 min (right) stimulation with EGF or insulin at indicated doses. b) Western blot of phosphorylated AKT (Thr308 and Ser473) and TBC1D4 (Thr642) in 3T3-L1 adipocytes following EGF or insulin stimulation at indicated doses for 5 (top) or 15 (bottom) min. GAPDH* loading control for pTBC1D4, pAKT308 and pERK, GAPDH** loading control for pAKT473. One-way ANOVA with Dunnett’s post-hoc text, *p < 0.05; **p < 0.01. Figure 4a is not convincing. The commercial C3ORF18 antibodies currently available are suboptimal for Western blotting. Nevertheless, we have optimised our workflow (using 4-20% gradient gels, increased concentration of primary antibody to 1/500) to generate a better, more convincing blot, clearly showing an increase in C3ORF18 protein during adipocyte differentiation (New Fig. 4a). We have also included corresponding qPCR data, showing increased C3orf18 mRNA over 3T3-L1 differentiation (New Fig. 4b). Figure 4b should display controls and comparison with GLUT4. We have revised this figure to include GLUT4 (New Fig. 4c). We are not sure which additional controls the Reviewer is suggesting. The data in figure 4c relate to my point above. The effect of insulin is small, ~70%. This is of a magnitude observed for endosomal proteins, like the transferrin receptor. Is this insulin-specific? This could be an example whereby the authors address the issues noted above. We agree with the Reviewer that the magnitude of the HA-C3ORF18 translocation response in original Figure 4C is consistent with the magnitude observed for endosomal proteins like TfR. Indeed, this is also consistent with the kinetics of HA-C3ORF18 translocation more closely matching TfR, than GLUT4, and the colocalisation of C3ORF18 with TfR-positive structures (New Figure 4d-i). Please also see our response to Reviewer 2 below for further discussion of C3ORF18 localisation. Regarding the specificity of the response to insulin or other growth factors. We have studied whether EGF-stimulation invokes GLUT4 and/or C3ORF18 translocation. These data are described above (Reviewer Figure 1) and included in the revised manuscript (New Extended Data Figure 4l). These data suggest that the C3ORF18 translocation response is not specific to insulin. Figure 5. I would like to see the authors also present this data so that 5a-5d are normalized to the control (0 insulin) for each data set. The knockdown data are on average lower for all parameters. These are not statistically significant, but a lower basal value means that the fold increase in 2-DG, GLUT4 levels, etc., may not be as significantly different as the absolute values show. When analyzed this way, is there a difference between control and knockdown? There may well be, but this should be presented. Are GLUT4 levels changed when C3ORF18 is knocked-down? We understand and agree with the reviewers' points. In short, we have now included data in the revised manuscript that C3ORF18 knockdown lowers GLUT1 and GLUT4 abundance (New Figure 5f). This likely explains the slightly lower basal 2-DG in knockdown cells. We also observe a substantial reduction in insulin signalling to AKT and TBC1D4 (New Figure 5g, New Extended Data Figure 5j). Therefore, lower overall insulin-stimulated glucose transport and GLUT4 translocation is likely due to the combined lower glucose transporter expression and insulin signalling responses, especially at lower more physiological insulin doses. As the Reviewer requested, we have plotted the 2-DG uptake data as fold-change relative to basal as the reviewer requested (Reviewer Figure 2A-D). We have also plotted the surface TfR and GLUT4 data in this way. For completeness, we have also plotted the absolute change in 2-DG uptake at each dose of insulin (Reviewer Figure 2E-H). In almost all cases, with the exception of SGBS 2DOG uptake, the fold change data show a significant or a trend towards reduced responses with C3orf18 knockdown. However, these fold-change data are highly variable as the basal values are very low and close to the limit of detection in our assays, and a small change in the basal signal has a large effect on the fold change. This is especially the case for 2-DG uptake data (and especially in SGBS assays) where basal DPM values are close to the background counts. The results of these analyses are summarised below: 3T3-L1 2-DG data: These data show no effect on fold-change, although a trend towards reduced fold changes with C3orf18 knockdown, especially at 100 nM insulin. Absolute changes in 2-DG with insulin were impaired in knockdown cells. Although these fold-change may indicate a normal insulin response, please note the very high variability between experiments, largely due to low basal counts. SGBS 2-DG data: These data show no effect on fold-change, but impaired absolute change in 2-DG. Although these fold-change may indicate a normal insulin response, please note the very high variability in the C3ORF18 KD condition, due to very low basal counts. 3T3-L1 GLUT4 data: These data show impaired fold-change and absolute change in surface GLUT4 with C3orf18 knockdown. 3T3-L1 TfR data: These data show impaired fold-change and absolute change in surface TfR with C3orf18 knockdown. Overall, although noisy due to low signal in basal samples, these fold-change data indicate that total expression of glucose transporters and/or TfR are likely contributing to the overall lower responses observed with C3ORF18 knockdown, as the reviewer suspected. Since we are now including additional data and discussion on GLUT4 abundance, we have decided to keep the raw data presented in the manuscript (Figure 5a-d) and not include the fold-change data (which is supplied below for the reviewers). Please see below for our updated description of these data in the text on page 7: Decreased insulin-stimulated 2-deoxyglucose transport after 8 d C3orf18 knockdown was likely driven by both lower protein abundance of the glucose transporters GLUT4 and GLUT1, independent of mRNA expression (Fig. 5f, Extended Data Fig. 5i), as well as reduced proximal insulin signalling (Fig. 5g, Extended Data Fig. 5j). Lower insulin signalling responses were independent of changes in INSR mRNA and protein abundance or localisation (Extended Data Fig. 5k-m), but were associated with decreased IRS1 protein, as well as reduced expression of adipocyte marker genes (Extended Data Fig. 5n-p). Reviewer Figure 2: Fold change in fluorescence intensity of surface GLUT4 (a) and TfR (b) in control and C3orf18-depleted 3T3-L1 adipocytes stimulated with 0.5 or 100 nM insulin for 30 min compared to unstimulated (basal) cells (n = 6 biological replicates). Fold change in 2-deoxyglucose (2-DG) uptake in response to insulin in 3T3-L1 (c) and SGBS (d) adipocytes following C3ORF18 depletion (n = 8 and 5 biological replicates in 3T3-L1 and SGBS adipocytes, respectively). Absolute increases in fluorescence intensity of surface GLUT4 (e) and TfR (f) in control and C3orf18-depleted 3T3-L1 adipocytes stimulated with 0.5 or 100 nM insulin for 30 min compared to unstimulated (basal) cells (n = 6 biological replicates). Absolute increases in 2-deoxyglucose (2-DOG) uptake in response to insulin in 3T3-L1 (g) and SGBS (h) adipocytes following C3ORF18 depletion (n = 8 and 5 biological replicates in 3T3-L1 and SGBS adipocytes, respectively). All data represented as mean ± SEM. n.s. non-significant; *p < 0.05; **p < 0.01; ***p < 0.01; ****p < 0.0001 by two-way ANOVA and Holm-Šídák post-hoc tests. Figure 1c reveals that the plasma membrane is enriched in fraction 8, with little evidence of markers in fraction 7 (pink line in 1c). Why does insulin stimulation not enrich GLUT4 and IRAP in fraction 8 (figure 2b)? Figure 1c shows that the proteosome (purple) is enriched in fraction 8, not the plasma membrane (pink). The plasma membrane is more enriched in fraction 4, with some minor enrichment in fractions 1 and 2. The GLUT4 and IRAP profiles, for example in Figure 2b, show leftward shift in profile with insulin, from fraction 8 towards the earlier fractions. This is consistent with movement to the plasma membrane – although we cannot conclude this from the LOPIT profile data alone. Minor issues: Figure 2D is not discussed in the text. Figure 2d is referred to in the results: “Differentially localised proteins were enriched for signalling terms (Fig. 2c), including insulin signalling, and for proteins with insulin-regulated phosphosites (Fig. 2d, Supplementary Table 1).” The legend for Figure 2d is: “Enrichment of proteins with insulin-regulated phosphorylation sites in 3T3-L1 adipocytes as reported by Fazakerley (2023)10 or Humphrey (2013)1 in all proteins vs. high-confidence differentially localised proteins (diff. loc. prob. = 1). Fisher’s exact test, Fazakerley p = 2.2 x 10-11, Humphrey p = 1.3 x 10-13.” We have added additional text to more clearly emphasise the data in 2d. See page 4: Differentially localised proteins were enriched for signalling terms (Fig. 2c), including insulin signalling, and for proteins previously identified to contain insulin-regulated phosphosites in 3T3-L1 adipocytes (Fig. 2d, Supplementary Table 1). This reviewer did not understand what should be taken from Figure 3A; could you improve the legend? Thank you for pointing this out. We have now improved the legend as requested for Fig. 3a for greater clarity: a) Chord plot visualising movement of proteins predicted to relocalise with high-confidence (diff. loc. prob. = 1) assigned to an organelle in at least 1 condition. The coloured lines indicate movement between compartments, with the arrowhead indicating direction of movement. For example, the two dark green blocks emanating from the “Golgi Apparatus” indicate movement of proteins from the Golgi to “Undefined” or the “Plasma Membrane”. Reviewer #2 (Remarks to the Author): This manuscript entitled “Dynamic subcellular proteomics identifies novel regulators of adipocyte insulin action” presents a comprehensive study of insulin-regulated protein redistribution in 3T3-L1 adipocytes using advanced subcellular proteomics approaches, including LOPIT-DC and surface biotinylation. The findings are exciting and likely to be of broad interest. The authors provide a detailed map of the subcellular localization of the adipocyte proteome, where many proteins align well with their expected localizations. The strength of the approach is further supported by the observation of known insulin-induced protein relocalizations and phosphorylation events. Notably, the identification of a novel candidate regulator, C3ORF18, as an insulin-responsive protein is intriguing and may open new avenues for research in insulin signaling and trafficking. Thank you very much for your positive assessment of our study. I commend the authors for their efforts to validate the identity of C3ORF18, a protein with a predicted molecular weight (MW) of ~17 kDa, observed as a ~40 kDa species. However, Figure 4a is not entirely convincing. While there is an increase in signal intensity upon insulin stimulation, the smear-like appearance of the signal lacks a clear, discrete band, which complicates interpretation. Given that C3ORF18 is barely detectable under basal conditions (as shown in Figure 4a and Supplementary Figure 4c), it is unclear how the authors conclude that it undergoes subcellular relocalization. We understand the concern regarding the ‘smear-like’ band for C3ORF18. We have generated a better, more convincing blot, clearly showing an increase in C3ORF18 protein during adipocyte differentiation (New Fig. 4a). We have also included corresponding qPCR data indicating increased C3orf18 expression over adipocyte differentiation (New Fig. 4b). We apologise for the confusion caused between Fig 4a and Extended Data Fig. 4c. - Fig. 4a showed increased expression of C3ORF18 over differentiation of 3T3-L1 adipocytes. Here we observe very low protein abundance in preadipocytes, with increasing expression during differentiation. - Extended Data Fig. 4c showed the plasma membrane fraction of differentiated 3T3-L1 adipocytes (day 10) which were untreated (basal) or treated with 100 nM insulin for 20 min. These data show that C3ORF18 abundance increased in the plasma membrane fraction of differentiated 3T3-L1 adipocytes following insulin treatment. We have updated the legend of Extended Data Fig. 4c: Western blot analysis of C3ORF18 in the affinity-purified PM fraction of differentiated (day 10) 3T3-L1 adipocytes isolated for plasma membrane proteomics as described in Fig. 3b. Furthermore, there appears to be an inconsistency between Figures 4a and 4b: C3ORF18 is not expressed in 3T3-L1 cells at day 0 in Figure 4a, yet it is detected in siControl cells in Figure 4b. Could the authors clarify this discrepancy? Is there a corresponding change in C3ORF18 MS intensity between basal and insulin-stimulated conditions? We again apologise that this was not clear from the Figure legends. The data in Fig 4a show increased expression of C3ORF18 over differentiation of 3T3-L1 adipocytes. At day 0, these cells are pre-adipocytes, where we could not detect the C3ORF18 protein. The data in 4b are micrographs of differentiated adipocytes (corresponding to day 10 in Fig 4a). Here we are showing the localisation of endogenous C3ORF18 in basal and insulin-stimulated cells. As Fig. 4b does not have siRNA knockdown conditions – the Reviewer may be referring to Extended Data Fig. 4b. This Western blot shows the abundance of C3ORF18 in differentiated adipocytes treated with control siRNA or siRNA targeting C3orf18. All siRNA knockdowns were performed post-differentiation, with the first siRNA transfection on day 6, and again on day 10. Experiments were performed on day 14 after differentiation. Therefore, the discrepancy identified by the Reviewer is explained by the “day 0” and “siControl” conditions in 4a and 4b being distinct. In 4a, the day 0 is undifferentiated cells, and the siControl in 4b is differentiated adipocytes subjected to non-targeting siRNA. We have updated the legend for Extended Data Fig. 4b: Western blot analysis of C3ORF18 expression in 3T3-L1 adipocytes with siRNA-mediated depletion of C3ORF18. Knockdown was performed on day 6 and day 10 of differentiation and cells were harvested on day 14 (8 d knockdown). 10% SDS-PAGE gel used to resolve proteins, full membrane shown on right. Additionally, the nature of the apparent MW shift observed for C3ORF18 should be addressed. What post-translational modifications (e.g., glycosylation) could account for this substantial shift on SDS-PAGE? It would be useful to treat the samples with glycosidases to determine whether the migration pattern changes, supporting the presence of glycosylation or other modifications. We thank the reviewer for their comment. We have been puzzled by this observation since starting our work on this protein. We now include additional data in the manuscript to provide more detailed analysis of C3ORF18 post-translational modifications and to try and shed light on the molecular weight shift. 1. N-linked glycosylation likely contributes to the molecular weight shift since C3ORF18 has a canonical N-link glycosylation motif in its lumenal N-terminal domain. Indeed, treating lysates from HEK-293 cells overexpressing HA-C3ORF18 with PNGaseF to remove all N-linked glycosylation results in an approximate 2-3 kDa decrease in the apparent C3ORF18 molecular weight. Mutation for Asn47 to Ala had the same effect of the apparent molecular weight, and this mutant was insensitive to PNGaseF. These data indicate that C3ORF18 is glycosylated at Asn47, but this only partly explains the higher-than-expected molecular weight on SDS-PAGE, suggesting that additional biochemical properties of C3ORF18 must underlie the major mobility shift. 2. Proteins can also be glycosylated on Ser/Thr residues. To test for O-linked glycosylation, we used a kit-based method to remove O-glycans, which relies on β-elimination, a base-induced chemical cleavage of the O-glycosidic bond that frees the sugar from Ser and Thr residues while preserving both the protein and glycan. This treatment did not affect the apparent molecular weight of C3ORF18 on SDS-PAGE. 3. Lysine residues can be modified in several ways, including the addition of small proteins like ubiquitin and SUMO that can substantially alter protein MW. To test whether lysine modification may explain increased C3ORF18 molecular weight, we generated a K-null mutant. Removing all lysines did not affect the apparent molecular weight of C3ORF18 on SDS-PAGE. 4. Phosphorylation can lead to large changes in electrophoretic mobility. However, C3ORF18 is not reported to be phosphorylated on phosphositeplus. Accordingly, dephosphorylation of protein lysates from cells expressing HA-C3ORF18 using lambda phosphatase did not affect the apparent molecular weight of C3ORF18 on SDS-PAGE. 5. Since the apparent weight of C3ORF18 appeared somewhat consistent with a dimer (17 + 17 = 34 kDa), we tested whether C3RORF18 homodimerises. We expressed two differently tagged C3ORF18 fusions in HEK cells (HA-C3ORF18; Alfa-C3ORF18) or a C3ROF18 fusion with both epitope tags (HA-C3ORF18-Alfa) and immunoprecipitated transiently expressed C3ORF18 using an anti-HA antibody. We did not detect an anti-Alfa signal in the precipitate, unless the HA and Alfa tags were on the same C3ORF18 molecule (i.e., duel tagged). These data suggest C3ORF18 does not homodimerise. 6. Finally, we tested whether intrinsically disordered regions in C3ORF18 may cause decreased migration by SDS-PAGE, as described for other proteins (PMID: 11420437). The N-terminal region of C3ORF18 is highly disordered as seen on IUPRED2a. Removal of the N-terminus almost completely restored C3ORF18 SDS-PAGE migration to its expected molecular weight. These data suggest that a feature of the lumenal N-terminal region of C3ORF18, perhaps the disordered regions, which are enriched in hydrophilic and charged residues that bind SDS poorly, is responsible for decreased electrophoretic mobility on SDS-PAGE. These data provide additional information on C3ORF18 assessment by Western blot and validation that the band observed at ~40 kDa is C3ORF18. are in New Extended Data Figure 4d-g. Text on page 6: Additional biochemical studies revealed that the N-terminal luminal portion of C3ORF18 was responsible for its reduced electrophoretic mobility, perhaps because of high disorder in this region59 (Extended Data Fig. 4d-g). Extended data figure legends: d) Intrinsic disorder prediction of C3ORF18 (upper panel) generated using the IUPred3A server (iupred.elte.hu)92. Y-axis represents the disorder probability score, and x-axis shows amino acid position. The grey horizontal line (0.5) indicates the disorder threshold, with regions above this value predicted to be intrinsically disordered. HA-C3ORF18 with indicated amino acid residues deleted (lower panel) revealed that the disordered N-terminus is responsible for most of the apparent molecular weight increase observed by SDS-PAGE. e) Deletion of all lysine (K) residues did not alter the apparent molecular weight of C3ORF18, ruling out lysine modifications as the cause of the molecular weight shift. f) PNGase treatment, and mutation of Asn47 to Ala (N47A) increased C3ORF18 migration by SDS-PAGE, indicative of N-linked glycosylation at Asn47. The shift in the apparent molecular weight after N-glycan removal suggests that N-linked glycosylation contributes, albeit marginally, to the higher-than-expected observed molecular weight for C3ORF18. g) O-glycan removal did not alter the apparent molecular weight of C3ORF18. To strengthen Figure 4b, I suggest including DAPI staining to help assess whether C3ORF18 localizes to the nucleus or perinuclear region. In the current images, the distinction is unclear, and co-staining would provide valuable spatial context. Moreover, in basal conditions, the apparent association of C3ORF18 with ribosomes (Figure 3d) suggests a possible localization to the rough ER, which aligns with its predicted membrane-associated nature. Colocalization experiments with ER markers, or proximity ligation assays (PLA), would help confirm this interpretation. To address the Reviewers comments, we have now performed more extensive colocalisation analysis at higher resolution for C3ORF18 using a series of subcellular markers (EEA1, TGN46, Calnexin), TfR and GLUT4. These data are presented in New Extended Data Figure 4i. C3ORF18 does not colocalise with calnexin, implying it is not ER-localised. The perinuclear staining of C3ORF18 is juxtaposed to TGN46 (as observed for TfR and G4), suggesting this is a post-Golgi compartment. C3ORF18 had limited overlap with the early endosome marker EEA1, but a high degree of colocalisation with TfR in both the perinuclear region and periphery. Comparatively, the overlap with GLUT4 in these same regions was lower. Importantly, this analysis also identified that C3ORF18 can be found in vesicles that are both TfR- and GLUT4-negative. This suggests that C3ORF18 may reside, at least partially, in a distinct vesicle population to both TfR and GLUT4. We have updated the text to describe these new data and to clarify the degree of C3ORF18 colocalisation with TfR and GLUT4. See page 6 of the text: To assess C3ORF18 subcellular localisation and validate the insulin-responsive relocalisation observed in our proteomics (Fig. 3c-e) and Western blot data (Extended Data Fig. 4c), we immunostained endogenous C3ORF18 in adipocytes under basal and insulin-stimulated conditions. This revealed C3ORF18 localised to both the perinuclear region and cell periphery, with increased peripheral staining in response to insulin (Fig. 4c-d). We observed similar localisation in adipocytes over-expressing HA-C3ORF18 (Extended Data Fig. 4h). HA-C3ORF18 did not colocalise with the ER-marker calnexin, and the perinuclear staining of HA-C3ORF18 was juxtaposed to the trans-Golgi marker TGN46 (Extended Data Fig. 4i), suggesting this is a post-Golgi compartment. HA-C3ORF18 had limited overlap with the early endosome marker EEA1 (Extended Data Fig. 4i), but endogenous C3ORF18 substantially colocalised with TfR and GLUT4, particularly in the perinuclear region (Fig. 4d). Quantitative analysis revealed greater colocalisation of C3ORF18 with TfR than GLUT4 (Fig. 4e), best exemplified when assessing C3ORF18 and TfR immunostaining in punctate structures towards the cell periphery under basal conditions (Fig. 4d). Importantly, this analysis also identified that C3ORF18 can be found in vesicles that are both TfR- and GLUT4-negative (Fig. 4d, inset). This suggests that C3ORF18 may reside, at least partially, in a distinct vesicle population to both TfR and GLUT4. The observed relocalization of C3ORF18 following inhibition of PI3K and AKT appears robust; however, a representative image would help the reader better appreciate the morphological differences under these conditions. We thank the reviewer for their suggestion. We have added images to complement the quantitative data in New Figure 4g. As an additional analysis, I recommend exploring C3ORF18 dependency using large-scale CRISPR screen datasets, filtered for adipocyte or metabolic cell types. This could offer insights into functional interactions or pathways related to C3ORF18. We thank the reviewer for their suggestion. We have looked into these datasets, specifically on the Welcome to the BioGRID Open Repository of CRISPR Screens (ORCS). The most biologically relevant cell type in which screening had been conducted is the myoblast line C2C12, where c3orf18 knockout resulted in accelerated mitophagy, implicating C3ORF18 as a negative regulator of this process (PMID: 31618756). However, a wide range of proteins involved in membrane trafficking were also identified in this screen (e.g., ESCRT proteins), so mitophagy may not represent the primary role for C3ORF18. We thank the reviewer for pointing us towards this information, but have decided to include this in our future studies and not refer to this in the current manuscript. Lastly, the term \"PM\" (plasma membrane) is used in the abstract without being defined. Please define it upon first use. Thank you for highlighting this omission. We have updated the abstract. Reviewer #3 (Remarks to the Author): In their study, Conway et al. use LOPIT, a subcellular proteomics approach, in a 3T3-L1 mouse cell model to screen for protein relocalizations following insulin stimulation. They identify C3ORF18, a protein of unknown function, as a potential novel player in insulin signaling. The manuscript is clearly structured, and the data are clearly presented and accessible via a Shiny app. The approach is interesting and allows for the unbiased identification of novel components involved in signaling. However, the biological relevance of the study is somewhat limited, and the overall outcome modest. The new insights gained are relatively minor, and the follow-up analysis of C3ORF18 is very superficial. The function of the protein remains unclear, and its role in insulin signaling is not convincingly established. In addition, the information about the used methods in the text itself is too short and difficult to follow, lacking the necessary detail to fully understand the data. Overall, while the technical approach is of interest, the study would benefit from a more thorough characterization of C3ORF18 and clearer methodological documentation. As it stands, the manuscript presents only limited biological insight. Specific points: Please provide a clearer explanation in the main text of how the LOPIT approach works, including the principle of fractionation, and how BANDLE assigns proteins to compartments based on distribution profiles. It is currently unclear how marker sets are chosen and whether they are assumed to remain static between conditions. We thank the reviewer for their comment and acknowledge greater explanation of the LOPIT-DC workflow and analysis is beneficial to reader interpretation. We have included more experimental and analytical details on page 3 of the text, in the sections entitled: Mapping the subcellular 3T3-L1 adipocyte proteome using LOPIT-DC and Insulin stimulates widespread protein relocalisation. • The number of markers used appears large, and it seems they are not reassigned under insulin stimulation. Does the method assume these markers are static and do not relocalize? If so, this is a potential limitation. The method assumes that marker proteins do not change condition under stimulation. This is not a limitation of the approach but a necessary modelling constraint. Marker proteins, by definition, are used to define a reference frame for subcellular niches in each condition, rather than as targets of differential localisation. Requiring that markers do not re-localise ensures identifiability of the underlying Bayesian mixture components and meaningful comparison of subcellular compartments across conditions. The number of markers used in this study is comparable to, and often less than that used in previously published subcellular spatial proteomics (PMID: 30659192, 39617061, 34599159, 38565923) and serves to improve robustness and reduce uncertainty in protein assignments. This assumption applies only to the markers and does not restrict the ability of the method to detect widespread or condition-specific re-localisation among non-marker proteins. We have added to our methods section to further explain how marker proteins were selected and used: The data was annotated with marker proteins, i.e. proteins well established to localise to a specific subcellular compartment, from previous subcellular proteomics experiments12,15,77. Additional marker proteins were identified through examining protein localisation in UniProt for proteins reported only to be localised to one organelle, and by mining the literature. Proteins were manually excluded as marker proteins if they appeared to relocalise in response to insulin. Under de Duve’s principle78, proteins residing in the same subcellular compartment are expected to share characteristic fractionation profiles. BANDLE leverages this by comparing the profiles of proteins with unknown localisation to those of curated organelle markers to infer subcellular assignments. • While some organelle profiles (e.g., mitochondria) are clearly separated, others, such as plasma membrane and Golgi, appear overlapping and are harder to distinguish. This is also reflected in their lower concordance with published data. Please provide a quantitative metric or quality score for the compartment assignments to support interpretability. We thank the reviewer for their comment. To quantitatively assess whether our dataset achieved sufficient separation of subcellular compartments we applied the QSep metric (PMID: 3071172) from the R pRoloc package to determine the separation of organelles in our subcellular proteomics datasets. The raw QSep score, represents the average Euclidean distance between clusters and therefore gives a direct measure of how far apart different compartments lie. QSep scores are then normalised, which refines this measure by taking into account the internal compactness of the reference cluster; it is calculated as the ratio of the average between-cluster distance to the average within-cluster distance. Thus, the normalised score reflects not only the separation of clusters but also how tightly each one is defined. For both metrics, larger scores indicate better resolution of compartments. We have added a boxplot summarising the distribution of normalised QSep values across all cluster pairs under both conditions in New Extended Data Figure 1a From the QSep scores, we conclude that most compartments are generally well-resolved in our dataset, with the nucleus, proteasome and, the cytosol being the best-resolved compartments. This plot is included in Extended Data Fig 1a, and are described in the manuscript in the following sections: Results - QSep, which quantifies the separation of clusters in subcellular proteomics data, was used to confirm the separation of organelles in our annotated data (Extended Data Fig. 1a). Methods – QSep calculates the Euclidean distance between raw protein profiles for each set of organelle markers, with higher values indicating better separation in a multi-dimensional space. For completeness, below, we provide the reviewers with all raw and normalised QSep values under both basal and insulin-stimulated conditions. Reviewer Figure 3: Heatmaps showing raw (left) and normalised (right) QSep scores under basal (top) and insulin-stimulated (bottom) conditions. The diagonal represents within-cluster distances (compactness), whereas off-diagonal values represent separation between different compartments. Colours range from blue (short distances) to red (large distances), with higher values indicating greater separation. • Line 111: If proteins in the ER–Golgi–PM axis show low concordance with other datasets, how can changes in localization be confidently attributed to insulin signaling rather than inherent trafficking variability? We thank the reviewer for their comment and understand the Reviewers concern. When referring to ‘low concordance’, we mean only in terms of allocation of localisation, not with regard to movement. It is true that proteins within the ER-Golgi-PM pathway are harder to resolve using the differentiation centrifugation method we have used here. We also know that proteins are highly mobile between these compartments, and that they may mutlilocalise. As such, we agree that mapping of proteins to these organelles is challenging. However, we are confident in attributing changes in localisation to the insulin stimulus because changes in localisation of a protein in response to insulin were not dependent on allocation of protein to an organelle. Protein movement was predicted in BANDLE based on individual protein profiles, and did not rely on information on protein localisation. We have clarified that the process of identifying protein relocalisation was independent of protein allocation to organelles in the text on page 4: “This Bayesian analysis framework compares the distribution profiles of proteins in unstimulated (basal) and insulin-stimulated adipocytes, and is independent of whether a protein has been allocated to an organelle (as in Fig. 1e).” • From ~4000 detected proteins, only ~1700 were assigned to compartments. Please explain why the remaining proteins could not be assigned. What thresholds or criteria (e.g., probability cutoffs) were used and how was it set? All proteins in BANDLE receive a localisation assignment, which is accompanied by several probabilistic measures, two of which are: • Allocation probability – the mean posterior probability that a protein belongs to a specific subcellular niche (i.e. how likely the protein is to reside in that organelle). • Outlier probability – the posterior probability that a protein belongs to the outlier component rather than any annotated organelle (i.e. the probability that the protein is not well described by any of the annotated subcellular niches). To assign proteins confidently to a single organelle, we filter based on both measures. Allocation probabilities closer to 1 indicate high confidence, while outlier probabilities are inverted during processing (1 - outlier), so values closer to 1 indicate stronger likelihood of being an outlier. As detailed in the Methods, proteins were assigned to a subcellular niche if their allocation probability was >0.9 and their outlier probability was below the median outlier probability for that organelle. This ensures that only proteins with high-confidence, single-organelle localisation are assigned, avoiding 'false-positive' assignments. Full annotated code for these steps is publicly available alongside the manuscript at https://github.com/CambridgeCentreForProteomics/adipocyteLOPIT2025/blob/main/4_processing_bandle/4_processing_bandle.Rmd. That only ~1,700 of ~4,000 detected proteins are assigned reflects both the conservative nature of probabilistic thresholding and biological reality. Many proteins reside in multiple subcellular locations (PMID: 32321741) or exhibit context-dependent localisation and are therefore not represented well by a single high-confidence assignment. Rather than being a limitation, this thresholding is deliberate: it avoids overconfident or misleading classifications, while still retaining the underlying probabilities for all proteins. The posterior probability for every protein is available in the Supporting Information Table 1 and the accompanying Shiny App https://proteome.shinyapps.io/adipocyte2025/, allowing readers to interrogate uncertain or multi-localised proteins quantitatively. Are proteins with lower assignment confidence also more likely to show differential localization? A majority of proteins predicted to be differentially localised were not assigned to an organelle in at least one condition. This is intuitive; many proteins redistribute partially between compartments rather than moving entirely from one organelle to another. Consequently, proteins with lower-confidence single-organelle assignments are indeed more likely to show differential localisation, reflecting both the biology and the probabilistic framework of the method. • Please describe in more detail in the text what the second proteomics method is. Can you compare the changes in the PM detected via LOPIT-DC and biotinylating? What is different and why? Why was it necessary in addition to LOPIT? What new information does it provide that LOPIT does not? We thank the reviewer for their comment. The LOPIT-DC technique identifies protein localisation and/or movement by comparing proteins distribution profiles across different obtained by differential centrifugation. This method is very accurate in localising proteins to organelles that have a very distinct profile across fractions, but less able to map proteins to less well resolved organelles (e.g., ER-Golgi-PM). Further, while it detects protein movement through altered protein profiles, these data are not quantitative and also often do not provide clear information on where proteins are moving to and from. Since we are particularly interested in proteins that are delivered to the PM in response to insulin (e.g., GLUT4), we undertook this complementary PM-specific analysis, which has been used extensively to profile changes in the PM proteome (PMID:22292497; 27462310; 32165698; 37302069). This provides 1) additional high-confidence assignment of proteins to the PM and 2) more quantitative data on the extent of changes in protein abundance at the PM. Using these two orthologous proteomics approaches concurrently enabled us to identify proteins which were both predicted to move in LOPIT-DC and significantly change in abundance at the PM, giving us greater confidence these proteins underwent insulin-stimulated translocation to the PM. We have included additional justification for and description of this technique in our revised manuscript (page 5): LOPIT-DC revealed the adipocyte PM proteome to be highly insulin responsive (Fig 3a, Extended Data Fig. 3a). To provide a more quantitative assessment of protein abundance changes at the PM in response to insulin, we used an orthologous cell surface biotinylation proteomics approach13 (Fig. 3b). Briefly, 3T3-L1 adipocytes were left untreated or stimulated with insulin (100 nM, 20 min) before surface proteins were biotinylated and isolated by affinity purification. • What happens to lipid droplets in the P1–P8 fractions? Could LD-associated proteins be counted more than once due to their presence in both the LD fraction and earlier fractions? We thank the reviewer for their comment. Whilst every care was taken to remove lipid droplets from fraction 1-8 and the cytosol, it is possible some LDs remain. This is why the abundance of LD proteins is not 0 in the earlier fractions. Further, as LDs bud from the ER, some LD-associated proteins are likely to be genuinely present in the non-LD fractions (i.e., not contaminants). Nevertheless, as shown in Extended Data Fig. 1b and 1c, classical LD proteins, including perilipins, are clearly enriched in the LD fraction. Due to the way that data from both centrifugation fractions and isolated LDs were combined and analysed ensures that these are not counted ‘twice’. The 10 fractions of each sample (P1-8, cytosol and LD) are each labelled with a TMT-tag and combined into one sample. Therefore, the amount of a LD protein across all fractions is quantified, and normalised to give its relative distribution across all fractions. • Septins are discussed in the context of lipid droplets. Do any septins relocalize in response to insulin? What is their possible role in adipocyte biology and lipid storage? We thank the reviewer for their comment. In our data, Septins were found to be present in the lipid droplet fraction in the basal state, but did not relocalise with insulin. Septin 11 has previously been implicated in adipogenesis by regulating neutral lipid loading onto lipid droplets (PMID: 27866222). We have now confirmed punctate localisation of both Septin 2 and Septin 11 on the LD surface in both murine and human adipocytes (New Figure 1f and Extended Data Figure 1h). Further, Sept2 knockdown during 3T3-L1 differentiation has revealed a role for Septin 2 in lipid droplet accumulation during differentiation, despite no obvious changes in adipogenic markers (New figure 1g and Extended Data Figure 1l-k). While our observations further lend weight to a role for Septins in lipid accumulation and lipid droplet biology, they do not provide additional mechanistic insight than already provided for Sept7, 9 and 11 (PMID: 40015624; 27866222; 35573204; 27417143). Further mechanistic studies will be required to understand the exact biology behind this process. • Interpretation of ER accumulation upon insulin: Some proteins show ER accumulation upon insulin treatment. How do you interpret this? Could this indicate a block or re-routing in vesicular trafficking rather than a classical translocation? We thank the reviewer for their comment. Indeed, this relocalisation, largely from the PM to the ER, could represent a slowing of traffic through the secretory system as suggested, although we might not expect to detect this signal after only 30 min insulin stimulation. Further work would be needed to ascertain the validity and mechanisms of these putative PM-to-ER translocations. • Protein abundance vs. localization change (Line 138): For IRAP, the differential localization probability is high, but the shift appears to reflect abundance rather than spatial redistribution. How does your model distinguish these scenarios? We thank the reviewer for their comment. The profile plots to display protein abundance across LOPIT fractions are normalised so that the total signal across all fractions =1 for both basal and insulin samples. In correlation profiling methods, we are interested in the normalised distribution pattern (i.e. relative normalised abundance), rather than raw abundances typically use as input for statistical analysis in a classic total proteome experiment. As such, it is not possible to observe changes in protein abundance from these plots. The Reviewer is correct that it is important to consider how changes in abundance and localisation interact over longer intervention periods. There is no change in total cellular IRAP or GLUT4- we have previously shown that there are no changes in protein abundance across the proteome with 30 min insulin stimulation (PMID: 36808134). We have included these data for IRAP and GLUT4 in New Extended Data Figure 2a. See page 4: Known insulin-responsive proteins including GLUT449 and LNPEP/IRAP50,51 were predicted to move with high-confidence (diff. loc. prob. = 1, Fig. 2b), validating our approach to detect insulin-responsive protein translocation. Changes in protein localisation were independent of IRAP and GLUT4 protein abundance (Extended Data Fig. 2a), and we have previously reported that total protein abundance is unchanged across the proteome in response to acute insulin stimulation10. • Line 146: What is the exact diff. loc. prob. threshold used to define significant relocalization? For proteins like mTOR, the localization shift is not obvious from the profiles—please clarify. We thank the reviewer for their comment. The diff. loc. prob. threshold's used to define relocalisation is described in the Methods and Results sections, as follows: Methods - Proteins were assigned to a subcellular niche if their allocation probability was >0.9 and their outlier probability was < median outlier probability for that organelle. Proteins were deemed differentially localised if differential localisation probability > 0. Proteins with differential localisation probability = 1 were considered ‘high confidence’ and all others as ‘candidates’ Results - We identified 899 out of 4005 proteins as candidate movers (differential localisation probability (diff. loc. prob.) > 0), of which 502 were high-confidence (diff. loc. prob. = 1) (Fig. 2a; Supplementary Table 1). The reviewer is right that some profiles, like mTOR, show more subtle responses. In this case the differential localisation probability of mTOR was 0.14, indicating this is a lower confidence (‘candidate’) mover. Nevertheless, there is a clear distinction between the profiles in fraction 4, in which the lysosome markers were enriched (Extended Data Fig. 2c). • Line 153: When proteins like DEPDC5 or SZT2 are said to dissociate from lysosomes, where do they relocalize? Is this to the cytosol or another compartment? We thank the reviewer for their comment. Unfortunately, as these proteins were not assigned to an organelle with high confidence, we are unable to confidently say where they relocalised to under insulin-stimulated conditions. The profile of these proteins show they are more abundant in fractions 6,7 and 8 in the insulin-treated cells. As marker proteins for the ribosomes, proteosome and Golgi are all enriched in these fractions, it is possible these proteins are relolcalising to one or more of these organelles. There is no increase in relative abundance of these proteins in fraction 9, the cytosolic fraction, so we cannot claim they relocalise to the cytosol. However, additional approaches (i.e., immunofluorescence) would be required to identify where these proteins relocalise to in response to insulin. • Line 195: Has C3ORF18 been identified in other proteomic studies of adipocyte differentiation? If so, please cite relevant references. C3ORF18 was identified by Klingelhuber and colleages (PMID: 38565923) in SGBS preadipocytes and adipocytes, and differentiated human adipocyte precursor cells in subcellular proteomic studies, but was not quantified in their studies on the adipocyte proteome during differentiation. We were unable to find other studies that had identified C3ORF18 in their analyses (e.g., PMID: 32384580; 32991178). • Figures 4b and 4e: The nuclear localization of C3ORF18 is suggested, but no nuclear marker is shown. Likewise, endosomal markers should be used to support the claim that the protein localizes to insulin-responsive endosomes, and • Clarify C3ORF18 relocalization dynamics: The current description is ambiguous. Does C3ORF18 move from the nucleus, endosomes, or another compartment to the plasma membrane? How do endosomes fit into the proposed mechanism? We thank the reviewer for their comments. We suggest C3ORF18 localises to the perinuclear region (around the nucleus), not within the nucleus. We have now performed more extensive colocalisation analysis at higher resolution for C3ORF18 using a series of subcellular markers (EEA1, TGN46, Calnexin), TfR and GLUT4. These data are presented in New Extended Data Figure 4i. C3ORF18 does not colocalise with calnexin, implying it is not ER-localised. The perinuclear staining of C3ORF18 is juxtaposed to TGN46 (as observed for TfR and G4), suggesting this is a post-Golgi compartment. C3ORF18 had limited overlap with the early endosome marker EEA1, but a high degree of colocalisation with TfR in both the perinuclear region and periphery. Comparatively, the overlap with GLUT4 in these same regions was lower. The colocalisation of C3ORF18 with peripheral TfR vesicles provides evidence that it localises to endosomes. As TfR is insulin-responsive, with similar insulin response and translocation kinetics to C3ORF18, we suggest that C3ORF18 may translocate to the PM with TfR from this endosomal compartment. See page 6 of the text: To assess C3ORF18 subcellular localisation and validate the insulin-responsive relocalisation observed in our proteomics (Fig. 3c-e) and Western blot data (Extended Data Fig. 4c), we immunostained endogenous C3ORF18 in adipocytes under basal and insulin-stimulated conditions. This revealed C3ORF18 localised to both the perinuclear region and cell periphery, with increased peripheral staining in response to insulin (Fig. 4c-d). We observed similar localisation in adipocytes over-expressing HA-C3ORF18 (Extended Data Fig. 4h). HA-C3ORF18 did not colocalise with the ER-marker calnexin, and the perinuclear staining of HA-C3ORF18 was juxtaposed to the trans-Golgi marker TGN46 (Extended Data Fig. 4i), suggesting this is a post-Golgi compartment. HA-C3ORF18 had limited overlap with the early endosome marker EEA1 (Extended Data Fig. 4i), but endogenous C3ORF18 substantially colocalised with TfR and GLUT4, particularly in the perinuclear region (Fig. 4d). Quantitative analysis revealed greater colocalisation of C3ORF18 with TfR than GLUT4 (Fig. 4e), best exemplified when assessing C3ORF18 and TfR immunostaining in punctate structures towards the cell periphery under basal conditions (Fig. 4d). Importantly, this analysis also identified that C3ORF18 can be found in vesicles that are both TfR- and GLUT4-negative (Fig. 4d, inset). This suggests that C3ORF18 may reside, at least partially, in a distinct vesicle population to both TfR and GLUT4. • Line 242: The effect of C3ORF18 knockdown on AKT signaling is claimed, but no clear quantification is shown, and the Western blot quality is suboptimal. Please provide quantitative data and clearer blots. We thank the reviewer for their comments. Quantification was shown in Extended Data Fig. 5c (now in New Figure 5g and Extended Data Figure 5j) and all blots are shown in the Source Data submitted alongside the revised manuscript. • Predicted interactors of C3ORF18: Many predicted interactors are mitochondrial proteins. How does this reconcile with its observed localization and proposed function in insulin signaling? We thank the reviewer for their comment. Both Biogrid (PMID: 33961781; HEK293 cells) and String (PMID: 36370105; Saccharomyces cerevisiae) databases include experimentally determined interactions between C3ORF18 and a subset of mitochondrial proteins. Firstly, these models may not recapitulate 1) endogenous C3ORF18 interactors as it is lowly expressed in HEK cells and 2) Saccharomyces cerevisiae may not recapitulate spatial organisation/interactome of a mammalian cell. Based on our current thinking about C3ORF18 function, we cannot currently explain these interactions. However, we note in our response to Reviewer 2, that C3ORF18 has been implicated in mitophagy in CRISPR screening, which may provide a link between C3ORF18 and mitochondria. Together, these observations will inform our future studies on C3ORF18 function. We do not think that these interactions are strong evidence of mitochondrial localisation of C3ORF18. Mitochondria are one of the best resolved organelles within LOPIT, and indeed subcellular proteomics methods in general, as there are a large number of mitochondrial-specific proteins in mammalian cells which can be used to train the localization classification algorithm. In our data 256 proteins were used as mitochondrial marker proteins, more than any other organelle (Supplementary Table 1). C3ORF18 was also identified in our plasma membrane proteomics where surface proteins were labelled with biotin before cells were lysed, further supporting C3ORF18 as a protein recycling with the PM and not localised to mitochondria. Additionally, immunofluorescence staining of C3ORF18 does not resemble the typical morphology seen for mitochondria in adipocytes. Therefore, we are confident C3ORF18 is not localised to mitochondria. • Line 248: The statement that C3ORF18 improves insulin sensitivity in adipose tissue is not supported by the presented data. This conclusion should be supported with additional experiments. We thank the reviewer for their comment. Our intention was to highlight that the data obtained from cell work and these clinical correlations were directionally consistent. We have edited out statement for accuracy: “These clinical correlative data are consistent with our data from cultured cells, indicating that C3ORF18 positively regulates insulin sensitivity in adipocytes.” • The functional role of C3ORF18 doesn’t become clear. Is its relocalization upon insulin treatment dependent on phosphorylation? Is its role primarily in signaling or trafficking? Based on your data, how might C3ORF18 mechanistically influence the insulin signaling cascade? A speculative model or hypothesis would strengthen the discussion. We do not yet have a complete picture of the function of C3ORF18. Its relocalisation to the plasma membrane is dependent on insulin signalling via AKT, but C3ORF18 itself has not been found to be phosphorylated in response to insulin. We do not know the exact signalling and trafficking mechanisms that underpin the C3ORF18 translocation responses. We have including additional text in our discussion to highlight the need for additional work here on Page 9. Future studies will need to address the exact mechanisms underpinning insulin-stimulated C3ORF18 redistribution and C3ORF18 linking C3ORF18 and endosomal traffic to longer-term adipocyte insulin sensitivity, and the role C3ORF18 plays in other tissues where it is enriched, such as muscle. We have provided additional data to try and link C3ORF18 and insulin signalling. Specifically, we have assessed the mRNA expression and protein abundance of key regulators of the insulin response in adipocytes upon C3orf18 knockdown in differentiated adipocytes. We have also performed additional functional assays after 4 d knockdown to try and reveal the primary effect of loss of c3orf18. 1. After 8 days KD, we observed impaired insulin-stimulated GLUT4 and TfR translocation, impaired insulin-stimulated glucose transport and impaired insulin signalling to AKT and TBC1D4. 2. As Reviewer 1 points out, in the case of GLUT4, TfR and 2DOG uptake, we observed some effects under of C3orf18 loss under basal conditions. Accordingly, the protein abundance of TfR, GLUT1 and GLUT4 was decreased by C3orf18 knockdown. For TfR, mRNA expression was also decreased. 3. To focus on insulin signalling, we measured the abundance and localisation of insulin receptor (INSR) and IRS1. C3orf18 knockdown did not affect INSR localisation, protein abundance or mRNA expression. However, there was a marked decrease in IRS1 protein. 4. In addition, C3orf18 knockdown decreased adipocyte marker gene mRNA expression when depleted from mature adipocytes. 5. In light of these wide-ranging effects, including transcriptional changes, after 8 days of C3orf18 knockdown, we conducted limited experiments after 4 days knockdown, when C3ORF18 protein remained at ~60%. At this timepoint we detected no change in insulin signalling or GLUT4 translocation or abundance. However, surface and total TfR abundance were decreased. These data suggest that TfR is more sensitive to loss of C3ORF18. These data are now all included in New Figure 5 and Extended Data Figure 5. Together, these data suggest that a primary effect of loss of C3ORF18 is altered TfR trafficking and/or stability, or at least that this pathway is more sensitive to C3ORf18 loss. Prolonged or more exaggerated loss of C3ORF18 decreased GLUT1 and GLUT4 protein in the absence of mRNA changes, suggesting increased protein turnover, and decreased insulin signalling responses, likely via lower IRS1 abundance. The combined effects of these changes in protein abundance impaired insulin-stimulated glucose transport. We have outlined our current proposed model for the role that C3ORF18 plays in maintaining adipocyte insulin sensitivity in the manuscript text in page 9: The molecular mechanisms linking C3ORF18 to adipocyte insulin responses remain unclear, although our data from cells depleted of C3orf18 for shorter periods suggest the primary effect of loss of C3ORF18 is an impairment in endosomal trafficking, as indicated by loss of TfR protein and impaired TfR delivery to the PM. Our working model is that C3ORF18 promotes proper endosomal recycling, and impairing this process over longer periods disrupts adipocyte insulin responses via a range of mechanisms, including 1) direct effects on membrane traffic; 2) loss of key insulin-responsive proteins that traffic via the endosomal system (i.e., GLUT4); and 3) transcriptional responses that suggest a role for C3ORF18 in maintaining mature adipocyte identity. Minor comments: · Line 43: phrasing should be improved. We have edited this sentence to improve clarity. “Insulin binding to its receptor, INSR, alters the subcellular distribution of specific proteins via several mechanisms. These include modulating protein-protein interactions via protein phosphorylation, protein recruitment to membranes through lipid messenger generation (e.g., PI(3,4,5)P3), and direct effects on membrane trafficking.” · Line 123 and 126: Extended data Fig.1h is mentioned before Fig.1g. We thank the reviewer for highlighting this error. The text has been corrected. · Please change colours of PM markers (basal vs insulin) in Fig. 3e so those can be distinguished by the reader. We thank the reviewer for their suggestion. The colours have been updated so the PM markers in basal are in pink, to reflect the colour for this organelle throughout the paper, and the PM markers in the insulin treated cells in blue, as used to show profiles following insulin treatment throughout the manuscript. · Any idea on what the band at 80-90kDa is in the C3ORF18 blots? We thank the reviewer for their question. From our experience with this antibody, including samples where we have knocked-down C3ORF18, we conclude this is non-specific band. We have now annotated this band in the revised manuscript figure (Extended Data Figure 4b). · Line 204: there is no figure 3f. We thank the reviewer for highlighting this error. The text has been updated to the correct figure."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made a careful, balanced and nuanced response to all of the points I raised. While I still remain a little 'on the fence' about the overall impact, the revised paper is a strong contribution to the field and therefore I am happy to indicate that in my opinion, the paper is now worthy of publication.", "reviewer_2": "The authors provided thorough and thoughtful responses to previous comments, and made substantial revisions to the manuscript. Overall, I believe the revised version represents a clear improvement. In particular, the authors have convincingly addressed my concerns regarding the biochemical identity of C3ORF18 and the apparent discrepancy between its predicted and observed molecular weight. The additional biochemical analyses (including glycosylation, lysine modification, phosphorylation, dimerization, and intrinsic disorder assessments) provide a comprehensive and convincing explanation for the altered electrophoretic mobility and significantly strengthen confidence in the specificity of the signal. The expanded high-resolution imaging and colocalization analyses substantially improve the interpretation of C3ORF18 subcellular localization. The use of multiple compartment markers, together with quantitative colocalization analyses, now clearly supports the conclusion that C3ORF18 resides predominantly in post-Golgi/endosomal compartments, with partial overlap with TfR-positive structures and limited overlap with GLUT4 and ER markers. The clarification that C3ORF18 localizes to a perinuclear region rather than the nucleus is also helpful. While the precise molecular function of C3ORF18 remains unresolved, I think the authors now provide a well-supported model that is appropriate for the scope of the study and represent a significant characterization of this protein.", "reviewer_3": "The authors have clearly invested significant effort in addressing the points raised during review. The technical concerns have been handled carefully, the LOPIT/BANDLE analysis is now much clearer, and the addition of orthogonal validation strengthens confidence in the dataset. Importantly, the revised manuscript avoids overstatement, and the limitations of the approach are now explicitly and appropriately discussed. These changes substantially improve the readability and credibility of the work. That said, while the new data provide additional insight into C3ORF18, a comprehensive mechanistic understanding is still missing. The authors convincingly demonstrate its localization, dependence on AKT signaling, and the consequences of C3ORF18 loss on TfR, GLUTs, IRS1, and insulin responsiveness. However, there is still no direct evidence for the underlying molecular mechanism, such as defined binding partners, involvement of specific trafficking machinery, or biochemical activity. In addition, the finding that C3ORF18 also responds to EGF introduces a conceptual shift, suggesting that it functions downstream of shared PI3K/AKT signaling rather than as an insulin-specific regulator. Overall, I appreciate the authors’ transparent presentation of the limitations of their study. While the work does not yet provide a complete mechanistic picture, it represents a solid and carefully executed contribution that will be useful to the field."}, "author_response": ""}]

Decision letter