Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO

Summary

The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.

Graphical abstract

Highlights

• •dSILO is a method to measure proteome-wide protein turnover rates in an organoid system
• •Turnover rates are overall higher in PDA metastatic organoids than in tumor organoids
• •Mitochondrial proteins have the highest turnover increase
• •Turnover changes are not generally correlated with changes in protein abundance

Motivation

We sought to profile protein abundance and turnover rate in organoids derived from a pancreatic cancer mouse model using dynamic SILAC labeling. By doing so in paired primary and metastatic tumor organoids derived from an autochthonous PDA mouse model, we aim to gain deeper, unbiased insights into potential mechanisms driving PDA metastasis.

Ross et al. present dynamic Stable-Isotope Labeling of Organoids (dSILO), a dynamic SILAC approach to measure proteome-wide protein turnover rates in organoids. They apply the method in a pancreatic ductal adenocarcinoma mouse model and discover that the metastatic organoids display accelerated proteome turnover compared to primary tumor organoids.

Introduction

Pancreatic ductal adenocarcinoma (PDA) is a devastating disease with a 5-year survival rate of only 12%.¹ This poor prognosis is partly related to the highly metastatic potential of PDA,^2,3 compounded by a frequent late-stage diagnosis of the disease.⁴ Understanding the cellular processes associated with metastatic transformation is paramount for developing effective treatment strategies and addressing the main causes of PDA mortality. Through genome sequencing efforts,^5,6 we now know that KRAS is the major driver oncogene in PDA, observed in over 90% of PDA cases.⁷ Other common mutations include CDKN2A (p14(ARF)/p16(INK4A)),⁸ TP53,^9,10 and SMAD4/DPC4.^11,12 Mutations in TP53 are the most prevalent in human PDA patients and are frequently associated with the loss of heterozygosity (LOH) of its wild-type allele.^13,14,15

Most PDA research has centered on genomic^{7,8,9,10,11,12} and transcriptomic^{16,17,18,19,20} profiling of primary tumors, which has provided fundamental insights into the genetic and molecular causes of PDA. However, it remains critically important to characterize the changes that drive metastatic lesions. Interestingly, next-generation genome sequencing of patient-matched primary pancreatic and metastatic lesions revealed striking genetic similarities between primary tumors and metastases,²¹ underscoring the crucial role of transcriptional and post-transcriptional changes in promoting PDA metastasis.^22,23,24,25 Indeed, global proteomic analysis of human PDA liver metastases revealed four distinct subtypes,²⁶ laying the groundwork for the potential application of proteomic classification in developing strategies to understand and target PDA metastasis.

The maintenance of protein homeostasis involves a constant balance between protein synthesis and degradation.²⁷ The disruption of this balance is linked to various diseases, including cancer^28,29,30 and neurodegenerative disorders.³¹ A growing body of evidence suggests that further disruption of this imbalance is a viable therapeutic strategy for cancer.^32,33,34,35

Recent advancements in proteomics have equipped us with tools to conduct comprehensive analyses of protein turnover in homeostatic systems.^27,36 One such technique is “dynamic stable-isotope labeling by amino acids in cell culture” (dSILAC).^{37,38,39,40,41,42,43,44,45} In this approach, cells are pulse-labeled with heavy stable-isotope amino acids, followed by mass spectrometric analysis. This allows for the measurement of protein half-lives at a proteome-wide level by comparing the rate of incorporation of heavy amino acids into new peptides against the reduction of light (unlabeled) amino acids. Combining dSILAC with isobaric labels such as tandem mass tags (TMTs) further enables reduced measurement time and sample-to-sample variability.^43,46

In this study, we introduce dynamic stable-isotope labeling of organoids (dSILO), an application of hyperplexed dSILAC-TMT to ex vivo organoid models. We apply dSILO to PDA to investigate if protein turnover is differentially regulated in the metastatic setting. Our findings suggest that the proteome of PDA metastases generally exhibits shorter half-lives compared to primary tumors. Specifically, the respiratory megacomplex I₂-III₂-IV₂,⁴⁷ also known as the “respirasome,”⁴⁸ is differentially regulated in PDA metastatic organoids. Given the plethora of recent studies reporting increased mitochondrial activity in metastatic cancer cells,^{44,49,50,51,52,53,54,55,56} our findings suggest that dynamic renewal of the respiratory chain components in PDA may represent a post-transcriptional mechanism to support the metabolic needs of metastases.

Results

Proteomic characterization of paired primary tumor- and metastases-derived PDA organoids

Using tissues from the Kras^G12D;p53^R172H;PdxCre (KPC) mouse model,¹⁴ we developed a panel of murine pancreatic ductal organoids derived from primary pancreatic tumors and paired distant metastases isolated from the liver or the diaphragm⁵⁶ (Figures 1A–1C). Malignant PDA is frequently, but not always, accompanied by a loss of heterozygosity (LOH) of TP53.^13,14,15 To separate out p53-driven effects from the differences between metastatic and primary tumors, we treated all cells with Nutlin-3a, a mouse double minute 2 (MDM2) antagonist,⁵⁷ for several passages (Figure 1A). As expected, Nutlin-3a selected for cells with Trp53 LOH (Figure 1D). We confirmed that Nutlin-3a did not significantly alter the composition of the metastatic proteome (Figure S1A). One out of five metastatic organoid lines (M19) retained one wild-type allele prior to Nutlin-3a treatment (Figure 1D), indicating that TP53 LOH is a frequent, but non-essential event of PDA metastasis, possibly due to loss of function mutations in downstream effectors such as CDKN2A.⁵⁸ We did not find strong differences in the proteome of the M19 organoid line compared to the other metastatic lines prior to Nutlin-3a treatment (Figure S1B).

Figure 1.

Biological model and experimental design

(A) Workflow for preparation of organoid lines for dSILO and global proteomics. Paired organoid cultures were established from the Kras^G12D; p53^R172H; PdxCre (KPC) mouse model of pancreatic cancer, comprising primary tumors (n = 4), diaphragm (n = 2), and liver metastases (n = 3). Organoid cultures were treated with Nutlin-3a for selection of cells with Trp53 LOH. The resulting panel of paired tumors (n = 4) and metastases (n = 5) was used for dSILO (protein turnover) and global proteomics (protein abundance) analyses.

(B) Representative hematoxylin and eosin (H&E) staining of parental tissues (primary tumors and metastases, “Met”) used for the establishment of organoid lines. Bright-field (BF) pictures of tumor and metastasis organoids used for treatment with Nutlin-3a. Scale bars: 100 μm for H&E pictures and 2 mm for BF pictures.

(C) Overview of primary and metastatic organoid pairs from each mouse in this study.

(D) PCR-based genotyping of primary tumor (“T”) and metastasis (“M”) organoids, before and after Nutlin-3a treatment. Upon treatment with Nutlin-3a, tumor organoids with the loss of their wild-type (WT) allele copy of Trp53 were enriched.

We looked at the total proteome changes among the 6,280 proteins identified in both primary tumor and metastatic organoid lines (Table S1). 167 proteins exhibit a significantly lower expression level (p < 0.05 and log₂FC < −0.58, 1.5-fold decrease) in metastatic tumors, while 48 proteins exhibit a significantly higher expression level (p < 0.05 and log₂FC > 0.58, 1.5-fold increase) (Figure S1C).

dSILO measurements show faster proteome turnover in metastases than primary tumors

To determine if the proteome is differentially turned over in metastatic PDA cells, we used dSILO to label five pairs of primary tumor and metastatic organoids in culture (Figure 2A). Cells were pulse-labeled over seven time points. Extracted lysates were subsequently TMT labeled, HPLC fractionated, and measured by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Quantifying TMT-labeled samples at the MS2 (tandem mass spectrometry) level can lead to signal interference from peptides co-isolated and co-fragmented along with the target peptide.⁵⁹ We addressed this with deep sample fractionation (Figure S1D), which reduces sample complexity, thereby enhancing accuracy and proteome coverage of samples.⁶⁰ TMT mixes were designed such that each primary tumor and its corresponding metastatic tumor(s) were in the same plex, along with a highly heavy-labeled “booster” sample, which was included to increase the coverage of heavy-labeled peptides picked for MS2 analysis (Figures S1E–S1K and Table S2).³⁹

Figure 2.

Experimental workflow and validation of method to determine protein half-lives in PDA organoids

(A) Experimental workflow for pulsing and quantification of protein half-lives in PDA organoids.

(B–F) Heatmaps for protein heavy (H)/light (L) ratios (ln(H/L + 1)) across all biological replicates over the course of 36 h.

(G) Coefficients of determination (R²) of the fit of the linear heavy/light label incorporation (in a semi-log plot; see STAR Methods for details) for each tumor and metastasis organoid line. Percentages represent the number of proteins with half-lives determined and R² >0.7. Data are shown as mean ± SD.

We identified a total of 49,976 unique peptides, mapping to 6,103 unique protein groups (Table S2). As expected with constant incorporation rates, heavy/light (H/L) ratios increased with time (Figures 2B–2F). We used a previously described model to calculate protein half-lives⁴² and extracted the R² values of the linear fit of heavy label incorporation as a filter for quality control, removing any proteins with an R² value <0.7. Over 90% of the identified proteins have an R² >0.7, suggesting high quality of the fit (Figure 2G). Of the 6,103 total proteins identified, 3,924 proteins pass the cutoff criteria of peptide spectrum matches (PSMs) > 2 and R² > 0.7 and were used for subsequent analyses (Figure S2A). We observed a good agreement of half-lives between biological replicates in primary (Figure S2B) and metastatic samples (Figure S2C). Consistent with previously published datasets,^45,61 we found that mitochondrial proteins are the most long-lived proteins in both primary and metastatic tumors, while cell surface proteins (including cell membrane, cell junction, and cell projection) are among the most short-lived proteins (Figures S2D and S2E).

Cell doubling rate plays a critical role in determining the degradation rate of proteins in dividing cells, especially those that turn over slowly.^27,62,63 We monitored the growth of organoids during heavy amino acid labeling and measured the change in organoid circumference and area over time using microscopy (Figure S3A). Although there is no significant difference in the proliferation between tumor and metastasis organoids as a group, metastatic organoids tend to double more slowly, with distinct differences across each line (Figures S3B–S3D). Thus, to account for variations across each line and ensure accurate measurement of protein degradation rates, we calculated the average degradation rates of the 20 most long-lived proteins in each organoid line and used these values as cell doubling correction factors (Table S2).

We compared the median protein half-lives between primary and metastatic organoids in a pairwise manner and observed that the median half-life of the metastatic proteome is significantly lower than the tumor proteome for every pair measured (p < 0.0001, paired Wilcoxon test) (Figures 3A–3E). To identify protein-specific changes, we focused on the median half-life of 2,349 proteins identified in at least three organoid pairs. In accordance with our global turnover analysis, the mean protein half-life of this subset of the proteome is 10.9 h lower in the metastatic organoids than their primary counterparts (Figure S2F). Together, these data suggest that PDA metastases have a faster proteome turnover rate than primary tumors. However, the higher turnover is not reflected in protein abundance, as there is a low correlation between these two parameters (Figures S3E–S3J).

Figure 3.

Mitochondrial proteins turn over faster in metastases compared to tumors

(A–E) Histograms of protein half-life frequency distributions for each metastasis vs. tumor pair. The “n” value in each graph corresponds to the number of proteins shared between each tumor and metastases pair. Differences between half-life distributions were assessed using the Wilcoxon test.

(F) Volcano plot of differentially turned over proteins in metastases compared to primary tumors. Blue dots indicate proteins with significantly decreased half-lives in metastasis compared to tumor (n = 486, p < 0.05, log₂FC < −0.32). Vertical red lines represent the bottom (left, log₂FC = −0.32) and top (right, log₂FC = 0.32) log₂FC cutoffs applied. The horizontal dotted line represents the p value cutoff (p = 0.05). p value established by paired t test.

(G) KEGG pathway enrichment analysis from DAVID showing pathways with shortened half-lives (i.e., faster turnover) in metastases compared to tumors. The x axis represents the −log₁₀(p value). Only pathways with p <0.05 are shown. “n” represents the number of proteins associated with each pathway.

(H) Keyword (KW) cellular compartment analysis from DAVID showing cellular compartments with shortened half-lives (i.e., faster turnover) in metastases compared to tumors. The x axis represents the −log₁₀(p value). Only cellular compartments with p <0.05 are shown. “n” represents the number of proteins associated with each cellular compartment. Mitochondrial and inner mitochondrial membrane compartments are marked in green.

Mitochondrial proteins show faster turnover in metastases compared to primary tumors

Of the 2,349 proteins commonly identified in at least 3 pairs of organoids, 486 proteins exhibit a significant 1.25-fold decrease (p < 0.05) in half-life in the metastatic setting (Figure 3F). No protein showed a significant increase in half-life in metastatic organoids, consistent with the general shift to shorter half-lives in metastases relative to tumors. The subset of proteins with significantly shorter half-lives is enriched for participation in metabolic pathways in the mitochondria (Figures 3G and 3H, Table S3), such as the tricarboxylic acid cycle and other metabolic pathways involved in carbon and amino acid metabolism (Figure 3G). Through cellular compartment analysis, we found an enrichment of mitochondrial and mitochondrial inner membrane proteins (Figure 3H). We tested the ratio of mitochondrial DNA (mtDNA) content relative to nuclear DNA (nDNA) in tumor and metastasis organoids and found no difference between the two cell types (Figure S4A). Therefore, the increased turnover of mitochondrial proteins in the metastatic setting is not due to a difference in mitochondrial numbers.

The mitochondrial respirasome megacomplex turns over faster in metastases than in primary tumors

Previous studies suggest that proteins with the capacity to assemble into complexes may exhibit longer half-lives compared to unassembled proteins.^{45,64,65,66,67} Other studies suggest that the half-lives of complexed proteins show higher coherence than expected by randomly assigning proteins to groups of the same size.^45,68 We examined our turnover data using the mammalian protein complexes database (CORUM)⁶⁹ and found that while proteins in complexes do not have significantly different half-lives than proteins without known protein binding partners (Figure 4A, Kolmogorov-Smirnov test, p > 0.05), proteins involved in known complexes do exhibit lower variance in half-life compared to randomized protein pairs (Figure 4B, Kolmogorov-Smirnov test, p < 0.05). This suggests a coherence in the turnover rates of proteins involved in complexes. To identify dysregulated complexes that may be contributing to metastatic dissemination, we investigated the differences in half-lives between primary tumors and metastases in the context of their associated complexes. We examined 53 protein complexes comprising >3 proteins, representing 407 total proteins, and identified 44 protein complexes with significant differences in overall turnover rates (Table S4). After condensing redundant complex assignments with minimal unique protein compositions, we noticed that 3 of the 10 complexes with the greatest differences between metastatic and primary tumors were components of the respirasome, the active form of mitochondrial complexes I₂-III₂-IV₂⁴⁸ (Figure 4C; Table S4). Overall, proteins from the respirasome have shorter half-lives in metastatic tumors compared to primary tumors (Figure 4D). We identified 20 proteins of the mitochondrial respiratory complex exhibiting shorter half-lives in metastases compared to primary tumors (p < 0.05 and |log₂FC| > 0.32) (Figure 4E). While not statistically significant, there are similar downward trends in the abundance of these proteins.

Figure 4.

Mitochondrial respiratory chain complexes turn over faster in metastases than in primary tumors

(A) Half-life cumulative distributions of proteins found to be in complexes and those without protein binding partners per CORUM. Proteins found within complexes did not display significant differences in half-lives compared with randomly grouped (shuffled) proteins within the dataset (Kolmogorov-Smirnov test, p > 0.05).

(B) Variance cumulative distribution of half-lives of proteins found to be in complexes per CORUM compared to randomly shuffled groups of proteins from the dataset. Proteins found within complexes show less variance in their half-lives than randomly grouped (shuffled) proteins within the dataset (Kolmogorov-Smirnov test, p < 0.05).

(C) Mean log₂ fold-change differences in half-lives of the top five protein complexes with significantly different turnover rates in metastases compared to primary tumors after curating for redundant complex assignments (Kolmogorov-Smirnov test, p < 0.001).

(D) Half-life comparison of differentially turned over respirasome proteins in tumors and metastases (two-sided paired t test, p < 0.05).

(E) Log₂ fold changes (FCs) of half-lives and protein abundances of overlapping proteins from the datasets of respiratory chain complexes I–V. Significance was assessed using a two-sided paired t test (p < 0.05). Data are shown as mean ± SD.

Previous reports have shown sub-architectural differences in the regulation of subunits of the mitochondrial respiratory chain (RC) complex I^65,70,71 and other complexes.⁴⁵ To determine whether these differences are present in our model, we mapped the results from differential turnover analysis of the subunits of these complexes between metastatic and primary tumors onto the cryo-EM structure of the assembled respirasome (PDBID: 5XTI ⁴⁷) (Figure S4B). We did not observe any clear trend in turnover rates associated with differences in the architecture of the respirasome.

Stability of mitochondrial proteins in light of previously published datasets

In this study, we found that metastatic cells showed faster overall proteome turnover than primary tumors (Figures 3A–3E and S2F), even though the metastatic organoids grew at similar or slightly slower rates (Figures S3A–S3D). Specifically, we found that mitochondrial proteins were among the most long-lived proteins in both the primary tumor (Figure S2D) and metastasis (Figure S2E). However, mitochondrial respiratory chain proteins were found to turn over significantly faster in metastases relative to primary tumors (Figures 4C–4E). To understand whether this observation was consistent in other biological systems, we compared our results to various published datasets.

A previous study by Welle and colleagues compared the turnover rates of proliferative and quiescent human dermal fibroblasts.⁴⁶ Their data indicate that quiescent cells have a faster turnover rate (higher k_deg) than proliferating cells, both globally (Figure S4C) and specifically for mitochondrial respiratory chain proteins (Figure S4E). This is in line with our observation (Figure S4D) and reinforces the notion that the rate of cell division and the rate of protein degradation are not necessarily positively correlated.

Next, we compared our dataset to a recent study by Dong and colleagues,⁷² who applied a pulse of isotopically labeled leucine to study the turnover of midbrain organoids derived from human induced pluripotent stem cells. Comparing the 312 proteins common to both datasets, we observed that the median half-lives of brain organoids are higher than those of PDA organoids (Figure S4F). Only one protein from the mitochondrial respiratory chain is common to both datasets, SDHA, with a half-life of 139.6 h in the midbrain organoids, much higher than in the tumor (114.8 h) and metastatic (78.2 h) PDA organoids. In line with our results, the authors also identify mitochondrial proteins as having the longest half-lives.

Finally, we compared our proteome turnover results to healthy liver tissue measurements from Rolfs et al.⁷³ We analyzed 841 proteins that are present in both datasets and compared their half-lives. Our analysis showed that the half-lives of liver tissue proteins are generally longer than that of metastatic organoids but shorter than that of primary tumor PDA organoids (Figures S4G and S4H). Both studies concurred that mitochondrial respiratory chain proteins are among the most stable in the proteome (Figure S4I).

Our findings suggest that the link between turnover and proliferation as well as the stability of mitochondrial proteins align with previous research. We hypothesize that a shared mechanism may account for the slow turnover of mitochondrial proteins in all three reference datasets and our primary tumors, which could potentially be disrupted in metastatic cancer.

Discussion

We developed dSILO to perform a proteome-wide characterization of protein turnover differences between pancreatic primary tumor and metastasis-derived organoids and show that steady-state proteome turnover occurs more rapidly in metastatic organoids than in primary counterparts.

Although some studies^61,65 suggested a connection between protein abundance and rate of turnover, our research has found that these two parameters are not always coupled in PDA cells. This is also true in the brain, where protein levels for genes associated with age-related neurodegenerative diseases remain constant, but their turnover rates change dynamically.⁷⁴ Therefore, it is important to measure both protein abundance and turnover to provide an accurate understanding of how gene expression is regulated in different cell types. The relationship between these two factors appears to be unique to each context.

Elevated mitochondrial respiration in metastasis has been reported in various cancers.^{44,49,50,51,52,53,54,55,56} Our findings show that the turnover of mitochondrial proteins is higher in metastatic PDA cells. This is consistent with the increased respiratory needs of metastatic cancer. The increased turnover of mitochondrial proteins could be a result of higher levels of reactive oxygen species production in the mitochondria of metastatic PDA cells, as has been previously reported.⁵⁶ Indeed, the genetic depletion of the mitochondrial quality control protein HSP60 (also known as HSPD1) impairs PDA cell proliferation and migration.⁷⁵ Although HSP60 is not identified in our turnover analysis, other members of the quality control machinery, such as LONP1, HSPA9, PITRM1, and PARK7, exhibit significantly faster turnover in metastasis (Table S3). Lastly, our findings suggest that the regulation of the respirasome as a whole may vary in the context of metastatic PDA, contrary to previous studies suggesting that there are functional differences between subunits of RC complexes at the sub-architectural level.⁶⁵

Despite organoid models being more costly and time-consuming compared to monolayer cultures, organoid cultures are a valuable resource as they maintain key features of the tissue of origin, including cell polarity and intra-tumor heterogeneity.² We anticipate that our method of measuring proteome turnover using dSILO will be useful in numerous comparative organoid studies, to determine how changes in proteome homeostasis contribute to developmental and pathological processes. Our research also opens up the possibility of using dSILO to investigate proteome turnover differences in personalized medicine development using patient-derived organoids.

Limitations of the study

To determine which protein complexes have the greatest differences in turnover between conditions, we sorted proteins into complexes using the CORUM database as a reference. However, it is important to note that this analysis may not be completely exhaustive. These databases are biased toward protein complexes that have been investigated and published more frequently. This means that there could be complexes that would invalidate the trends we observed. Nonetheless, these databases are among the few tools we have to assess protein complex dynamics. Furthermore, although we did not detect any clear trend in turnover rates linked to differences in the architecture of the respirasome, this conclusion is based on limited observations since only a few membrane-bound RC proteins were identified in our dataset.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Chemicals, peptides, and recombinant proteins
L-Lysine-2HCL	Thermo Scientific	Cat# 89987
L-Arginine-HCL	Thermo Scientific	Cat# 89989
L-Lysine-2HCL, 13C6, 15N2	Thermo Scientific	Cat# 88209
L-Arginine-HCL, 13C6, 15N4	Thermo Scientific	Cat# 89989
Nutlin-3a	Sigma-Aldrich	Cat# SML0580-5MG
Dithiothreitol	Fisher	Cat# BP172-5
Iodoacetamide	Sigma-Aldrich	Cat# I6125-25G
Sequencing Grade Modified Trypsin	Promega	Cat# V511X
Formic acid	Fisher	Cat# A117-50
TMT16plex mass-tag labeling reagent	Thermo Scientific	Cat# A44520
SYBR green	Thermo Scientific	Cat# 4367659
Advanced DMEM/F12	Gibco	Cat# 12634010
Penicillin/Streptomycin	Gibco	Cat# 15140163
GlutaMAX	Gibco	Cat# 35050061
HEPES	Gibco	Cat# 15630080
Gem21 neuroplex	Gemini Bio	Cat# 400-160
N-Acetylcysteine	Sigma-Aldrich	Cat# A9165
Gastrin	Sigma-Aldrich	Cat# G9145
EGF	PeproTech	Cat# 315-09
FGF10	PeproTec	Cat# 100-26
Nicotinamide	Sigma-Aldrich	Cat# N0636
Growth Factor Reduced (GFR)-Matrigel	Corning	Cat# 356231
SILAC media	Thermo Fisher Scientific	Cat# A2494301
Critical commercial assays
Quick-DNA™ Miniprep Plus Kit	Zymo Research	Cat# D4068
BCA kit	Pierce	Cat# PI23227
Deposited data
Protein turnover data of proliferative and quiescent human dermal fibroblasts	Welle et al.46	ProteomeXchange Consortium identifier: PXD004725
Protein turnover data of human induced pluripotent stem cells (iPSC)-derived midbrain organoids	Dong et al.72	ProteomeXchange Consortium identifier: PXD032169
Protein turnover data of healthy mouse liver tissue	Rolfs et al.73	MassIVE identifier: MSV000086426
Proteome expression and turnover datasets	This study	MassIVE ID number: MSV000092498
Experimental models: Cell lines
Mouse: KPC derived PDAC organoids	He et al.56	N/A
Oligonucleotides
Trp53 loxP Fw: AGCCTGCCTAGCTTCCTCAGG	He et al.56	N/A
Trp53 loxP Rv: CTTGGAGACATAGCCACACTG	He et al.56	N/A
CO1 Fw: TGCTAGCCGCAGGCATTAC	Jovanovic et al.80	N/A
CO1 Rv: GGGTGCCCAAAGAATCAGAAC	Jovanovic et al.80	N/A
NDUFV1 Fw: CTTCCCCACTGGCCTCAAG	Jovanovic et al.80	N/A
NDUFV1 Rv: CCAAAACCCAGTGATCCAGC	Jovanovic et al.80	N/A
Software and algorithms
Original code for data analysis	This study	Figshare DOI: https://doi.org/10.6084/m9.figshare.25245997.v1
ImageJ	National Institutes of Health	https://imagej.nih.gov/ij/; RRID: SCR_003070
GraphPad Prism	GraphPad	http://www.graphpad.com/; RRID: SCR_002798
R studio	Posit	https://posit.co/download/rstudio-desktop/
Spectronaut	Biognosys	https://biognosys.com/shop/spectronaut
Xcalibur	Thermo Fisher Scientific	https://chemistry.unt.edu/∼verbeck/LIMS/Manuals/XCAL_Quant.pd; RRID: SCR_014593
MaxQuant	Max Planck Institute of Biochemistry	https://maxquant.org/; RRID: SCR_014485
Other
25 cm sub-1.6-μm Aurora C18 column	IonOpticks	Cat# AUR2-25075C18A
25 cm sub-2-μm Aurora C18 column	IonOpticks	Cat# AUR2-25075C18A
Axygen Gel Documentation System	Corning	Product number GDBL-1000

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Marina Ayres Pereira (marinaayrespereira@gmail.com).

Materials availability

All organoid lines can be obtained via a CUIMC materials transfer agreement (free of charge for non-commercial purposes).

Data and code availability

• •The proteome expression and turnover datasets that support the findings on this study was deposited to MassIVE under the ID number MSV000092498. Data are publicly available as of the date of publication. Accession numbers are listed in the key resources table. This paper analyzes existing, publicly available data. The accession numbers for these datasets are listed in the key resources table.
• •All original code for data analysis was deposited on Figshare (DOI: https://doi.org/10.6084/m9.figshare.25245997.v1) and to the Jovanovic Lab github account: https://github.com/mjlab-Columbia/2023_dSILO. Code is publicly available as of the date of publication. DOIs are listed in the key resources table.
• •Any additional information required to reanalyze the data reported in this work is available from the lead contact upon request.

Experimental model and study participant details

Organoid isolation and culture

Detailed procedures to isolate and propagate murine primary and metastatic pancreatic organoids have been described previously.^56,76 Briefly, organoids were maintained in complete organoid media: Advanced DMEM/F12 (Gibco, Cat# 12634010) supplemented with 1% Penicillin/Streptomycin (PS) (Gibco, Cat# 15140163), 1x GlutaMAX (Gibco, Cat# 35050061), 1x HEPES (Gibco, Cat# 15630080), Gem21 neuroplex (Gemini Bio, Cat# 400-160), 1.25 mM N-Acetylcysteine (NAC) (Sigma-Aldrich, Cat# A9165), 10 nM gastrin (Sigma-Aldrich, Cat# G9145), 50 ng mL-1 EGF (PeproTech, Cat# 315-09), 10% RSPO1-conditioned media, 20% Noggin-FC-conditioned media (the Noggin-Fc-expressing cell line was a kind gift from Dr. Gijs R. van den Brink, University of Amsterdam), 100 ng mL-1 FGF10 (PeproTech, Cat# 100-26), and 10 mM Nicotinamide (Sigma-Aldrich, Cat# N0636). To passage, organoids were washed out from the Growth Factor Reduced (GFR)-Matrigel (Corning, Cat# 356231) using ice-cold PBS, mechanically dissociated into small fragments using fire-polished glass pipettes, and then seeded into fresh GFR-Matrigel. Passaging was performed at a 1:4 split ratio roughly twice per week. All experiments described were done without EGF and NAC.²⁴ To isolate p53 LOH (loss of heterozygosity) organoids, early-passage tumor (Pdx1-Cre; Kras^+/LSL−G12D; Trp53^{+/LSL−R172H}) organoids (as previously described⁵⁶) were cultured in complete organoid media with 10 mM Nutlin-3a (Sigma-Aldrich, Cat# SML0580-5MG) and propagated for at least three passages (or until confirmation of p53 LOH by PCR). After verification of p53 LOH, organoid lines were allowed to recover for three passages before being seeded for experiments. All cells were cultured at 37°C with 5% CO₂.

Method details

Genotyping

Organoids were harvested from three wells of a 24-well plate and centrifuged at 1500 xg for 5 min at 4°C. Organoids were washed three times with ice-cold PBS, after which genomic DNA was extracted using the Quick-DNA Miniprep Plus Kit (Zymo Research, Cat# D4068). Each PCR reaction for Trp53 1loxP genotyping was performed in a 20 μL mixture containing 1x GoTaq G2 Hot Start master mix, 0.5 μM each primer, and 100ng template DNA. p53 LOH was confirmed by PCR¹⁴ using the following primers.

• •Trp53 loxP Fw: 5′-AGCCTGCCTAGCTTCCTCAGG-3’
• •Trp53 loxP Rv: 5′-CTTGGAGACATAGCCACACTG-3′

The PCR cycling conditions were 95°C for 2 min, followed by 34 cycles at 95°C for 30 s, 62°C for 30 s, and 72°C for 15 s, with a final extension step at 72°C for 5 min. PCR products were separated on a 2% agarose gel in 1X TAE buffer, and gel imaging was performed using an Axygen Gel Documentation System.

Dynamic stable isotope labeling in organoids (dSILO)

Murine PDA primary and metastatic organoids were grown in EGF and NAC-free media to unmask mitogenic and redox-dependent mechanisms.²⁴ When organoids reached the desired confluency, the media was changed to SILAC media (Thermo Fisher Scientific, Cat# A2494301) containing either light (0.50 mM L-Lysine-2HCL, Thermo Scientific, Cat# 89987 and 0.70 mM L-Arginine-HCL, Thermo Scientific, Cat# 89989) or heavy-labeled (0.50 mM L-Lysine-2HCL, 13C6, 15N2, Thermo Scientific, Cat# 88209 and 0.70 mM L-Arginine-HCL, 13C6, 15N4, Thermo Scientific, Cat# 89989) amino acids. The remaining growth factor composition of the SILAC media is identical to the organoid culture media described above. Cells were treated with heavy-labeled media for 0, 3, 6, 12, 18, 24, and 36 h. All wells experience the same number of media changes at each time point and were harvested simultaneously. Multiple ice-cold PBS washes were performed to ensure Matrigel removal. Snap-frozen cell pellets were stored at −80°C until they were subsequently processed for protein extraction and mass spectrometric analysis.

Organoids growth assessment

In order to assess the sizes of organoids in both tumor and metastasis settings, we used UCB Vision Science’s "Hough Circle Transform" plugin for Fiji (ImageJ). We identified all the organoids in the microscopy images at multiple focus levels and measured their relative radius in pixels. This is then used to calculate total area and circumference.

Sample processing and mass spectrometry measurements

Cells were lysed for 30 min in urea buffer (8 M urea; 75 mM NaCl, 50 mM Tris HCl pH 8.0, 1 mM EDTA) with 1X Protease Inhibitor Cocktail (Sigma, Cat# P8340). Lysates were centrifuged at 20,000 g for 10 min, and protein concentrations of the clarified lysates were measured via BCA assay (Pierce, Cat# PI23227). Protein disulfide bonds of the lysates were reduced for 45 min with 5 mM dithiothreitol (Fisher, Cat# BP172-5) and alkylated for 45 min with 10 mM iodoacetamide (Sigma, Cat# I6125-25G). Samples were then diluted 1:6 with 50 mM Tris HCl, pH 8.0, to reduce the urea concentration to <2 M. Lysates were digested overnight at room temperature with trypsin in a 1:50 enzyme-to-substrate ratio (Promega, Cat# V511X) on a shaker. Peptide mixtures were acidified to a final volumetric concentration of 1% formic acid (Fisher, Cat# A117-50). Tryptic peptides were desalted on C18 StageTips as previously described,⁷⁷ and evaporated to dryness in a vacuum concentrator.

For global expression data, approximately 1 μg of total peptides were analyzed on a Waters M-Class UPLC using a 25 cm sub-1.6-μm Aurora C18 column (IonOpticks Cat# AUR2-25075C18A) coupled to a benchtop Thermo Fisher Scientific Orbitrap Q Exactive HF mass spectrometer. Peptides were separated at a flow rate of 400 nL/min with a 160 min gradient, including sample loading and column equilibration times. Data was acquired in data-independent mode (DIA) using Xcalibur 4.5 software. MS1 Spectra were measured with a resolution of 120,000, an AGC target of 5e6 and a mass range from 350 to 1650 m/z. Per MS1, 38 equally distanced, sequential segments were triggered at a resolution of 30,000, an AGC target of 3e6, a segment width of 36 m/z, and a fixed first mass of 200 m/z. The stepped collision energies were set to 22.5, 25, and 27.

For proteome turnover (dSILO) analysis, desalted peptides were labeled with the TMT16plex mass tag labeling reagent according to the manufacturer’s instructions (Thermo Scientific, Cat# A44520) with small modifications. Briefly, 0.2 units of the TMT16plex reagent was used per 20 μg of sample. Peptides were dissolved in 30 μL of 50 mM HEPES pH 8.5 solution, and the TMT16 plex reagent was added in 12.3 μL of MeCN. After 1h incubation, the reaction was quenched with 2.4 μL 5% Hydroxylamine for 15 min at 25°C. Differentially labeled peptides were mixed for each replicate. To reduce peptide complexity and achieve deeper proteome coverage, samples were then separated by basic reversed-phase chromatography as described previously,⁶⁰ with 15 final concatenated fractions (Figure S2A). Approximately 1 μg of total peptides per sample were then analyzed on a Thermo Scientific Orbitrap Q Exactive HF mass spectrometer coupled via a 25 cm sub-2-μm Aurora C18 column (IonOpticks, Cat# AUR2-25075C18A) to an Acquity M Class UPLC system (Waters). Peptides were separated at a flow rate of 400 nL/min with a linear 95 min gradient from 2% to 22% solvent B (100% acetonitrile, 0.1% formic acid), followed by a linear 30 min gradient from 22 to 90% solvent B. Each sample was run for 160 min, including sample loading and column equilibration times. Data was acquired in data-dependent (DDA) mode using Xcalibur 4.1 software. MS1 Spectra were measured with a resolution of 120,000, an AGC target of 3e6, and a mass range from 300 to 1800 m/z. Up to 12 MS2 spectra per duty cycle were triggered at a resolution of 60,000, an AGC target of 1e5, an isolation window of 0.8 m/z, and a normalized collision energy of 28.

TMT16 plexes were designed so that every primary tumor and its corresponding metastatic tumor were run in the same mix (Figure S2B, Table S1). A strongly labeled “booster” spike-in channel was added to the first and last channels of each mix in order to increase the chances of heavy peptides being picked for quantification,³⁹ and to increase the likelihood of the same peptides picked for quantification across mixes (Figures S2C–S2H). One mouse included in the study had metastatic tumors from both the peritoneum and liver and as such organoid samples from its primary tumor were run in two separate TMT16 plexes (Figure S2B, Table S2).

Identification and quantification of proteins

Global expression data were searched using Spectronaut version 17.2 using the default BSG settings and median normalization. dSILO data were searched with MaxQuant software version 1.7.0 using a modified mouse UniProt database (March 2013) with added entries for mutant KRASG12D and p53R172H. MS/MS searches for the proteome datasets were performed with the following parameters: TMT16 labels were added as isobaric labels and quantified on MS2. Oxidation of methionine, protein N-terminal acetylation, and heavy SILAC amino acids (Lys8 and Arg10) were added as variable modifications. Carbamidomethylation was added as a fixed modification. Trypsin/P was selected as the digestion enzyme. A maximum of 3 labeled amino acids and 2 missed cleavages per peptide were allowed. The mass tolerance for precursor ions was set to 20 ppm for the first search (used for nonlinear mass recalibration) and 6 ppm for the main search. Fragment ion mass tolerance was set to 20 p.p.m. For identification, we applied a maximum FDR of 1% separately on the protein and peptide levels. We required 2 or more unique/razor peptides for protein identification and a ratio count of 2 or more for protein quantification per replicate measurement.

Protein half-life determination

Calculation of half-lives was performed as previously described.⁴² Briefly, starting with the evidence.txt file of the MaxQuant output, PSM entries were adjusted to reflect their relative fraction of corresponding MS1 values. Heavy-labeled PSMs were filtered to only contain fully labeled peptides. Subsequently, heavy and light peptides of PSMs with the same protein group IDs and charge states were matched so that H/L ratios could be calculated. PSM-level H/L ratios were then median-summarized into protein-level H/L ratios.

The rate constant of protein degradation (k_deg) was obtained by linear regression using the equation:

$$k_{\mathit{deg}}=\frac{\sum _{i=1}^m{\mathit{log}}_e\left(r_{t_i}+1\right)t_i}{\sum _{i=1}^m{t}_i^2}-\frac{{\mathit{log}}_{e}2}{t_{cd}}$$

where m is the number of pulsing timepoints (t_i) and r_ti the ratio of H/L amino acids for a specific protein at each timepoint, and t_cd is the estimation of cell doubling. Protein half-life (T_1/2) then was calculated as per Schwanhausser et al.⁴²

$$T_{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}=\frac{{\mathit{log}}_{e}2}{k_{\mathit{deg}}}$$

To estimate cell doubling times (t_cd), proteins for each sample were ranked according to their kdeg. The 20 proteins with the lowest kdeg for each sample were selected, and the mean value was used to estimate cell doubling.

Only proteins with at least 3 PSMs measured by MS were considered for half-life quantification. As a quality test, the coefficient of determination R² was calculated for each fit. Only proteins with half-lives with a R² > 0.7 were considered. As the resolution associated with very long half-life measurements is not compatible with the short-time courses used in the present study, proteins with very long half-lives (>200 h) were excluded from the subsequent comparative analysis between tumors and metastasis. The matrigel contaminants Col4 and Laminin were filtered out.

KEGG pathway, UP_KW_Cellular compartment term enrichment, and MitoPathway analysis

KEGG pathway analysis and UP_KW_Cellular compartment analysis were performed using the DAVID Bioinformatics Database (DAVID Bioinformatics Resources, https://david.ncifcrf.gov/).⁷⁸ Calculation of over-represented KEGG pathways and UP_KW_Cellular compartment was done using the entire list of proteins with half-lives calculated as background (threshold count = 2, and EASE score = 1). Pathways and GO terms with p < 0.05 were selected.

MitoCarta3.0 was used for annotation of mitochondrial proteins.⁷⁹ Assessment of mitochondrial pathways was performed using information retrieved from MitoCarta3.0_MitoPathways.

Analysis of half-life distribution within protein complexes

Proteins identified in our dSILO dataset were cross-referenced with complexes annotated in the CORUM database.⁶⁹ Consequently, a single protein could potentially be associated with multiple distinct protein complexes. To examine differences in half-life distributions between complexed and uncomplexed proteins, we compared those proteins found to be involved in one or more complexes per CORUM (n = 841) to those proteins not found to have any binding partners in CORUM (n = 1508), and applied Kolmogorov-Smirnov tests to assess significant differences in the half-life distributions between the two groups. To assess the overall coherence of half-lives of proteins involved in complexes compared to those proteins not associated in complexes, we randomly shuffled any CORUM-annotated proteins found in the dataset proteins into groups and compared their variance distribution to those of proteins found in true complexes, then performed Kolmogorov-Smirnov tests to assess significant differences between the two groups. To identify those complexes with the greatest aggregate differences in turnover rates between primary and metastatic tumors, we performed Kolmogorov-Smirnov tests to assess significant differences in the half-life distributions between each organoid line of the primary tumors and metastases. Analysis was performed in Python 3.10.

Mitochondrial:Nuclear DNA Rrtio Qqantification

We quantified the mitochondrial DNA (mtDNA)/nuclear DNA (nDNA) ratio as described previously⁸⁰ with slight modifications. Total cellular DNA was extracted using the Quick-DNA Miniprep Plus Kit (Zymo Research, Cat# D4068). Primers against mitochondrial-encoded NDUFV1 and genomically-encoded CO1 were used to quantify mtDNA and nDNA, respectively. The following primer sequences were used.

• •CO1 Fw: 5′-TGCTAGCCGCAGGCATTAC-3’
• •CO1 Rv: 5′-GGGTGCCCAAAGAATCAGAAC-3’
• •NDUFV1 Fw: 5′-CTTCCCCACTGGCCTCAAG-3’
• •NDUFV1 Rv: 5′-CCAAAACCCAGTGATCCAGC-3′

Quantitative PCR was performed using an SYBR green-based detection reagent (Thermo Scientific Cat# 4367659) and an Applied Biosciences StepOne Plus Real-Time PCR system.

Quantification and statistical analysis

Statistical analysis of global expression and turnover data

Statistical data analysis was performed in R Studio 2022.07.2 or Prism 9.0.

For protein expression data, comparisons between primary and metastatic tumor organoids were performed on protein intensity levels, normalized with Spectronaut’s default median normalization settings, using a two-tailed paired t-test. Results are shown as mean ± SD. Proteins that met our threshold parameters of |log2FC| ≥ 1.5 and p < 0.05 were considered differentially expressed between conditions.

Half-live distributions between primary and metastatic tumors were compared using Wilcoxon-rank testing to compare half-live distributions generated as described in the section above. Correlations were computed using Prism 9.0, and a linear regression model was fitted using Prism statistics.

For complex analysis and for the identification of complexes with differences in turnover rates between primary tumors and metastases, we performed Wilcoxon-rank tests. A p < 0.05 was considered statistically significant throughout all analyses.

Published: April 25, 2024

Supplemental information

Table S2. Turnover dataset with half-lives of primary tumor and metastatic PDA organoid lines, related to Figures 2 and 3

Tabs include detailed TMT mix assignments, the list of proteins used for cell doubling corrections, and a list of long-lived proteins (T1/2 > 200h).