A shear stress-responsive pathway in monocytes drives cardiopulmonary bypass-induced inflammation via spectrin/RAF1/store-operated calcium entry

SUMMARY

Cardiopulmonary bypass (CPB) during cardiac surgery triggers inflammation that increases morbidity and mortality, though its molecular mechanisms remain unknown. To address this gap, we conducted single-nucleus RNA/ATAC sequencing (snRNA-seq/snATAC-seq) to profile transcriptional- and chromatin-level changes in circulating leukocytes from neonatal patients who underwent CPB. Classical monocytes increase after CPB, show dysregulated inflammatory genes, and exhibit altered chromatin accessibility, underscoring their role in CPB-associated inflammation. Expression of the proinflammatory cytokine interleukin-8 (IL-8/CXCL8) is significantly upregulated after CPB exposure, accompanied by increased accessibility of its promoter to AP-1 transcription factors. A genome-wide CRISPR screen in THP-1 cells identified SPTAN1 and RAF1 as novel effectors of hemodynamic stress. We further found that SPTAN1 and RAF1 activate store-operated calcium entry under CPB conditions, leading to elevated IL8 expression. We identify a shear stress-responsive SPTAN1/RAF1/store-operated calcium entry (SOCE) pathway and show that targeting it may alleviate CPB-induced inflammation, providing new insights into sterile inflammation and shear sensing in non-adherent cells.

In brief

Li et al. reveal that shear stress activates inflammation in monocytes via a pathway that involves non-erythroid spectrin (SPTAN1) and RAF1 stimulation of store-operated calcium entry and highlight therapeutic candidates that could alleviate cardiopulmonary bypass-induced inflammation.

Graphical Abstract

INTRODUCTION

Over 30,000 pediatric¹ and 150,000 adult patients² undergo cardiac surgery every year according to the Society of Thoracic Surgeons Database. These patients experience systemic inflammation that contributes to postoperative complications.³ The etiology of this inflammatory response is likely multifactorial, given that these patients experience cardiopulmonary bypass (CPB), surgical trauma, and ischemia-reperfusion. CPB is routinely used during cardiac surgery to give the surgeon a bloodless field in which to operate while minimizing ischemic damage to the body. In patients recovering from complicated cardiac surgery, increased cytokine levels are associated with high mortality and extended intensive care unit stays.⁴ Neonatal patients are particularly at risk, with a 10% mortality and a 30% complication rate.^5,6 Despite CPB being used for over 70 years,⁷ there are a number of open questions related to the pathogenesis of CPB-associated inflammation that have slowed efforts to ameliorate this systemic inflammatory response. Understanding the underlying mechanisms by which CPB induces inflammation is critical for efforts to reduce post-cardiac surgery complications in patients.

CPB represents a unique opportunity to study how innate immune activation contributes to systemic inflammation, since this process begins when CPB is initiated. Most of the clinical situations where inflammation is typically studied—such as sepsis, lupus, rheumatic arthritis, and Kawasaki disease—involve studying the resolution phase, since the patients cannot be identified prior to the start of inflammation. This barrier represents significant challenges to understanding the early steps in inflammatory processes. In contrast, studying patients who underwent CPB provides valuable insights into the initiation of sterile inflammation.

CPB involves exposing the blood of patients to a number of non-physiological insults, including supraphysiologic fluid shear stresses, which, as we have demonstrated, are sufficient to induce cytokine expression and necroptosis in non-adherent leukocytes.⁸ While previous studies have examined how adherent cells,⁹ red blood cells,^10,11 and platelets¹² respond to shear stress, it remains unclear how non-adherent nucleated cells respond to shear stress. Understanding the mechanisms by which non-adherent cells respond to shear would address fundamental questions related to mechanobiology.

To identify the mechanisms that underlie CPB-associated inflammation, we performed single-nucleus sequencing of samples collected from neonatal patients exposed to CPB in conjunction with in vitro mechanistic experiments. We first performed single-nucleus RNA/ATAC sequencing (snRNA/ATAC-seq) profiling of circulating peripheral blood mononuclear cells (PBMCs) from neonatal patients who underwent CPB. Exposure to CPB modulated gene expression in pathways related to inflammatory and immune signaling for all PMBCs clusters identified by snRNA-seq. In addition, snATAC-seq data showed that CPB also dynamically regulated chromatin accessibility. Of note, over 60% of the chromatin regions altered in response to CPB were found in the promoter regions of PBMCs. We conducted a series of analyses and experiments to study how these promoter regions undergo alterations, given their fundamental role in controlling gene expression and cellular function. In terms of chromatin accessibility, we found that the promoter region of IL8 was opened after shear stress by performing ATAC-seq on THP-1 cells, a human non-adherent monocytic cell line. Additionally, we identified several differential expression genes (DEGs) associated with a corresponding increase or decrease in promoter region accessibility in monocytes. AP-1-relevant genomic regions opened after shear stress at time points immediately following shear stress exposure but gradually returned to baseline during the recovery stage. Using the key proinflammatory cytokine IL8 as a readout, an in vitro genome-wide CRISPR screen performed in THP-1 cells revealed that shear stress activates inflammatory responses via a mechanism that involves a non-erythroid spectrin (SPTAN1)/RAF1 signaling pathway. Additional mechanistic experiments demonstrated that SPTAN1/RAF1 promotes Ca²⁺ entry into the cell interior through the store-operated calcium entry (SOCE) pathway, consequently upregulating IL8 expression. Based on the mechanisms of shear stress-induced inflammation in monocytes proposed here, we tested three molecules, all either US Food and Drug Administration (FDA) approved or undergoing clinical trials, for their potential of reducing shear-mediated inflammatory gene expression in blood samples exposed to shear stress in vitro. Taken together, these data provide novel insights into the pathogenesis of CPB-associated inflammation, revealing how non-adherent cells sense and respond to shear stress and presenting new potential strategies for mitigating inflammation during CPB.

RESULTS

Transcriptional changes and chromatin remodeling in PBMCs after CPB

To investigate how CPB modulates cellular composition, gene expression, and chromatin structure in a relatively unbiased manner, we performed snRNA-seq and snATAC-seq on PBMCs collected from neonatal patients (patient demographics in Table S1) at four time points: pre-CPB, at the end of CPB, 8 h post CPB, and 24 h post CPB (workflow outlined in Figure 1A). The study included four neonatal patients (aged ~3–6 days; three males, one female) with congenital heart defects such as double-outlet right ventricle with subpulmonary VSD (Taussig-Bing anomaly) plus interrupted aortic arch type B, and transposition of the great arteries (TGA). The CPB times for each patient ranged from 143 to 275 min. The pre-CPB samples served as a baseline control for each patient. 8 h after CPB was selected as a time point, since patients typically show signs of significant inflammation at this time. By 24 h post CPB, much of the inflammation has often resolved. Using snRNA-seq, we identified 13 distinct cell types from 124,526 cells across 16 samples (Figure 1B), and using snATAC-seq, we identified 11 cell types from 102,673 cells across 12 samples (Figure 1D), confirmed through the presence of marker genes and ATAC peaks, respectively. (Figures S1 and S2). Based upon quantification of cell counts for clusters derived from snRNA-seq data, the percentage of classical monocytes increased by 28% at the end of CPB and by 116% 8 h post CPB as compared to pre CPB, suggesting a pivotal role for classical monocytes in responding to CPB exposure (Figure 1C). Of note, other innate immunity-related cell clusters, including CD8⁺ NKT-like and CD8⁺/CD4⁺ NKT-like cells, were also larger at the end of CPB and 8 h post CPB as compared to pre CPB. These results preliminarily indicated that the innate immune system was activated in patients exposed to CPB at the single-cell level. DEG analyses of the snRNA-seq data showed that CPB exposure broadly modulates the immune system at the level of gene expression regulation. We identified 48–405 DEGs in each cell type as compared to the pre-CPB time point (adjusted p < 0.05; Figures 1E–1G). Inflammatory and immune pathways were significantly modified in cell types such as B cells, CD8⁺/CD4⁺ NKT-like cells, classical monocytes, effector/naive CD4⁺ T cells, and naive CD8⁺ T cells after CPB exposure (Figure 1H). Notably, the genes IL8, ADAMTS3, EREG, MIR646HG, and PADI4 were initially identified as targets of CPB-induced transcriptional regulation in total circulating leukocytes from patients exposed to CPB.⁸ The data from this study revealed that these genes were specifically upregulated in monocytes (Figures S3 and S4). Hence, these findings underscore a pivotal involvement of classical monocytes in modulating the immune system response to CPB in neonatal patients.

Figure 1. Profiling of snRNA-seq and snATAC-seq in neonatal patients who underwent CPB.

(A) Schematic of PBMC sampling from congenital neonatal patients who underwent CPB. PBMC samples from four stages were collected from each patient. Pre represents before heart surgery; Post represents after heart surgery; and 8 and 24 h represent recovery stages.

(B) Uniform manifold approximation and projection (UMAP) plot for cell clustering and cell type annotation of 124,526 cells based on snRNA-seq. Thirteen cell types were annotated.

(C) Distribution of cell fractions of annotated cell types based on snRNA-seq across four stages. The proportion of classical monocytes, CD8⁺ NKT-like cells, and naive CD4⁺ T cells are labeled, with values in parentheses representing the percentage increase in classical monocytes.

(D) UMAP plot for cell clustering of 102,673 cells based on snATAC-seq and cell type annotation mapping from snATAC-seq. Eleven cell types were annotated.

(E–G) DEGs were identified at Post, 8 h, and 24 h time points as compared to the baseline stage (Pre), respectively. The topportion represents the count of DEGs. The bottom portion represents the distribution of average log2FoldChange of these DEGs in each cell type.

(H) Functional enrichment results of these DEGs detected at Post, 8 h, and 24 h sampling points. Gradient yellow to blue color represent the value of −Log(P). The size of the dots represents the count of genes in each pathway. Statistical significance was accepted at p < 0.01. Res, response; Immu, immune; Acti, activity; Pos, positive; and Reg, regulation.

(I) Heatmap showing the count of open and closed ATAC-seq peaks identified at Post, 8 h, and 24 h time points across all cell types in PBMCs from patients exposed to CPB.

(J) Structure annotation of snATAC-seq peaks detected in all cell types among PBMCs from patients who underwent CPB. Over 60.07% peaks were located in promoter regions.

Given the transcriptional changes observed in PBMCs, we next investigated via snATAC-seq data whether exposure to CPB caused dynamic opening and closing of chromatin regions. The CPB conditions caused changes in 15–3,972 genomic regions across different cell types in PBMCs (Figure 1I). Over 60% of the differentially modulated chromatin accessibility peaks are in the promoter regions of genes (Figure 1J). These findings indicate that CPB-induced changes occur not only at the transcriptional level but are also accompanied by widespread remodeling of chromatin accessibility, highlighting the epigenetic regulation of immune and inflammatory responses in PBMCs.

Regulatory mechanism of promoter alterations and transcription factor binding in CPB

Gene expression is regulated by promoter accessibility, where transcription factor (TF) binding and epigenetic modifications influence RNA polymerase recruitment and mRNA transcription. In classical monocytes, we identified 25 DEGs at the post-CPB time point, with significantly altered chromatin accessibility at their promoter regions (Figure 2A). Of these 25 DEGs, 12 genes, including key inflammatory genes, exhibit significantly increased chromatin accessibility at their promoter regions (Figure 2B), including IL1R1 (adjusted p = 3.91 × 10⁻⁴⁸), which functions as the primary receptor for interleukin-1α (IL-1α) and IL-1β¹³; IL1R2 (adjusted p = 1.44 × 10⁻¹⁶⁷), which acts as a decoy receptor, binding IL-1 ligands and regulating inflammation¹⁴; and AREG (adjusted p < 1 × 10⁻³⁰⁰), which is upregulated in response to inflammatory stimuli and promotes the activation of monocytes, macrophages, and T cells.¹⁵ In contrast, 13 downregulated genes, including CD74 (adjusted p = 8.71 × 10⁻³⁶), CD83 (adjusted p = 8.24 × 10⁻²⁷³), and HLA-DPA1 (adjusted p = 4.35 × 10⁻⁵) present decreased promoter accessibility (Figure 2C). We further performed ATAC-seq on THP-1 cells in vitro to compare chromatin accessibility changes between shear stress and static conditions. Comparing the in vitro ATAC-seq peaks with snATAC-seq data from classical monocytes obtained from patients identified 212 open and 304 closed overlapping peaks (Figure 2D). For example, we found that ATAC peaks in the promoter regions of LINC01270 (adjusted p = 7.21 × 10⁻²³), DUSP8 (adjusted p = 3.46 × 10⁻¹³⁷), and TREML4 (adjusted p = 1.40 × 10⁻⁴²) opened at the end of CPB, while the peaks at MS4A4A (adjusted p = 2.85 × 10⁻³³) and SMIM35 (adjusted p = 1.04 × 10⁻¹⁷) closed upon shear stress in vivo and in vitro (Figure S5). These discoveries suggest that transcriptional regulation under shear stress conditions is accompanied by promoter accessibility shifts.

Figure 2. Shear stress induces shifts in chromatin accessibility and increased binding of AP-1 TFs.

(A) Candidate DEGs of classical monocytes in patients identified at the post-CPB time point.

(B) Promoter accessibilities increased in 12 genes, with increased gene expression in the post-CPB classical monocytes (see A).

(C) Promoter accessibilities decreased in 13 genes, with gene expression being downregulated after CPB (see A).

(D) Venn plots show the overlapping peaks between the sheared THP-1 cells in vitro and classical monocytes of patients who underwent CPB in vivo.

(E) TF enrichment analyses between Post and Pre time points (left) and between Post and 24 h time points (right). AP-1 TFs, including JUN, JUNB, JUND, FOS, FOSL1, and FOSL2, were significantly enriched in open snATAC-seq peaks at Post time points compared to Pre time points, while AP-1 TFs were significantly enriched in closed peaks at 24 h time points compared to Post time points. Top TFs are labeled.

(F) TF enrichment analysis of open and closed peaks detected in THP-1 cells using ATAC-seq. Top TFs are labeled in plots. AP-1 TFs were significantly enriched in open peaks (left).

(G) Footprinting analysis of JUN and FOS TFs in classical monocytes shows that the signal of JUN and FOS increased at Post time points in patients exposed to CPB.

(H) Footprinting analysis of JUN and FOS TFs in THP-1 cells shows that the signal of JUN and FOS increased in shear-stressed THP-1 cells.

Based upon promoter peaks, identified using snATAC-seq, in classical monocytes of patients exposed to CPB we found eight AP-1 TFs that are significantly enriched in open peaks, while the KLF, SP, and EGR TFs are enriched in closed peaks under post-CPB conditions (adjusted p < 0.01; Figure 2E). Notably, relevant AP-1 TF open peaks became closed and returned to baseline 24 h post CPB (adjusted p < 0.01; Figure 2E). In the THP-1 shear stress experiment in vitro, AP-1 family members were the most significantly enriched TFs in open peaks, while NF-E2 and MAFK were enriched in closed peaks (adjusted p < 0.01; Figure 2F). The JUN, JUNB, JUND, FOS, FOSL1, and FOSL2 TFs show enhanced activation signals in both CPB classical monocytes and shear-stressed THP-1 cells (Figures 2G, 2H, and S6). Since we had previously demonstrated that exposure to shear stress causes JUN translocation to the nucleus via a Ca²⁺-dependent mechanism,⁸ we performed CUT&RUN experiments using a JUN antibody that detected 125 open and 62 closed peaks in response to shear stress in THP-1 cells (Figure S7), further emphasizing AP-1 TFs as playing important roles in sensing fluid shear stress.

Genome-wide CRISPR screen identified SPTAN1

Among PBMCs, classical monocytes constituted the largest cluster and play a crucial role in the inflammatory response as part of the innate immune system in CPB conditions. We conducted a series of experiments to identify key CPB-responsive genes in monocytes. The expression of the IL8 gene was examined, since we have previously shown that its expression increased in bulk RNA sequencing (RNA-seq) data from neonatal patients who underwent CPB.⁸ Increased IL8 levels have also been linked to worse outcomes for patients exposed to CPB.^16–19 In this study, snRNA-seq of PBMCs demonstrated that IL8 expression increased in classical monocytes at the end of CPB as compared to pre-CPB and subsequently decreased from this peak at the 8 and 24 h post-CPB time points (Figure 3A). To validate the elevated IL8 expression triggered by CPB conditions, we mimicked CPB conditions in vitro by exposing cultured THP-1 cells to shear stress conditions. Using bulk mRNA-seq, we identified IL8 was significantly upregulated in response to shear stress, as well other 382 upregulated and 64 downregulated genes (Figure 3B). Furthermore, we predicted TFs associated with the upregulated genes identified in THP-1 cells following exposure to shear stress. Using ChEA3,²⁰ we found that AP-1 TFs (FOSB, ATF3, and JUN) were significantly enriched (Figure 3C). Additionally, the IL-8 promoter was significantly opened after shear stress exposure (Figure 3D). Based on these findings, we chose IL8 expression as a readout for an unbiased genome-wide CRISPR loss-of-function screen to explore how shear stress triggers a cascade of molecular reactions, ultimately leading to an inflammatory response in non-adherent monocytic cells.

Figure 3. Genome-wide CRISPR screen identified two novel genes, SPTAN1 and RAF1, as candidates for hemodynamic shear stress activation in THP-1 cells.

(A) UMAP plot highlighted with green represents classical monocytes. Feature plots on the right display the IL8 expression at four time points. The adjusted p value between Pre and Post is indicated (****adjusted p < 0.0001).

(B) Volcano plot showing DEGs identified in sheared THP-1 cells compared to static conditions using mRNA-seq in vitro. Three replicates were generated for each condition. 383 genes were significantly upregulated, while 64 genes were significantly downregulated. Statistical significance was accepted with p < 0.05 and log2FoldChange < −1 or > 1. The upregulated gene IL8 is labeled.

(C) TF binding prediction of the genes upregulated in sheared THP-1 cells shows that AP-1 TFs (FOSB, ATF3, and JUN) are significantly enriched.

(D) Plot of ATAC-seq data from sheared THP-1 cells demonstrates opening of the IL8 promoter as compared to static controls.

(E) Schematic of the genome-wide CRISPR screen using the promoter of IL8 as readout in THP-1 cells.

(F) Dot plot of CRISPR screen candidates ranked by p value. Of 1,628 genes, representing 8.5% of the total, gRNAs were underrepresented in the 10% GFP bright cells with adjusted p < 0.05. The top five genes and two novel important candidates are labeled.

(G) Selected key GO terms from the Metascape analysis of 1,628 candidates.

(H) Nine candidate genes are significantly enriched in the cell cortex pathway. The size of dots represents the adjusted p value.

(I) SPTAN1-KD THP-1 cells have a 41.20% reduction in shear-mediated activation of IL8 expression as compared to sheared wild-type (WT) THP-1 cells based upon qPCR. Replicates, n = 6. ***p < 0.005.

(J) Quantification of cellular necrosis, early apoptosis, and live cells demonstrates a 43.40% reduction in cellular necrosis and 10.30% increase in live cells after shear in SPTAN1-KD THP-1 cells as compared to control sheared cells. Replicates, n = 6. ****p < 0.001.

(K) Western blot demonstrating that SPTAN1-KD cells have decreased shear-mediated ERK1/2 phosphorylation as compared to control THP-1 cells. Replicates, n = 3. **p < 0.01, ****p < 0.001.

(L) KRAS colocalizes with SPTAN1 in THP-1 cells based upon immunofluorescence (IF) and proximity ligation assay (PLA).

(M) RAF1 colocalizes with SPTAN1 in THP-1 cells based upon IF and PLA.

(N) qPCR demonstrating that RAF1-KD cells have a 48.50% reduction in shear-mediated IL8 expression as compared to sheared control cells. Replicates, n = 6.

(O) Quantification of cellular necrosis, early apoptosis, and live cells demonstrates that RAF1-KD cells undergo 29.30% less shear-mediated necrosis and have 15.80% increased cell survival as compared to control THP-1 cells. Replicates, n = 6.

(P) Knockdown of RAF1 decreases shear-mediated ERK1/2 phosphorylation as compared to control THP-1 cells in a western blot. Replicates, n = 3. *p < 0.05, **p < 0.01.

The design of this CRISPR screen used the proximal IL8 promoter-driving green fluorescent protein expression (IL8-GFP) as a reporter in THP-1 cells (Figure 3E). THP-1 cells were transduced with lentiviruses driving a genome-wide CRISPR-Cas9 interference library²¹ and then exposed to shear stress. The cells were sorted based on GFP brightness and then sequenced. Genes whose gRNAs were underrepresented in the brightest GFP pool were classified as candidate genes important for the shear stress response. We identified 1,628 candidate genes with an adjusted p < 0.05 as cutoff (Figure 3F; Data S1). To our surprise, genes previously identified as shear stress sensors in endothelial cells, such as PECAM1, VEGFR2 (KDR), and PIEZO1,^9,22,23 were not identified as candidates in our CRISPR screen. The absence of endothelial “flow sensor genes” suggests that non-adherent circulating leukocytes utilize a different mechanism to sense shear stress as compared to adherent cells.

We subsequently performed functional enrichment analysis for the candidate genes identified in the CRISPR screen. The Gene Ontology (GO) terms “mitogen-activated protein (MAP) kinases” (p = 2.30 × 10⁻⁶) and “actin cytoskeleton organization” signaling pathway (p = 2.30 × 10⁻⁷) were significantly enriched (Figure 3G; Data S1). Several kinases in the pathway of “mitogen-activated protein (MAP) kinases”—namely, MEKK2, MEK2, JNK3, and MEK—were previously identified as being critical for fluid stress activation of THP-1 cells,⁸ which assisted with validating the results of the screen. The “actin cytoskeleton organization” pathway contains nine CRISPR screen genes potentially associated with shear stress: SPTAN1 (nonerythroid spectrin; p = 3.00 × 10⁻¹⁷), MYL2 (myosin II light regulatory chain; p = 3.00 × 10⁻⁴), ACTA1 (α-actin; p = 1.00 × 10⁻³), MYO1G (myosin 1g; p = 1.00 × 10⁻²), SEPTIN8 (p = 1.00 × 10⁻²), ACTN1 (α-actinin; p = 2.00 × 10⁻²), SEPTIN6 (p = 2.00 × 10⁻²), SEPTIN15 (p = 3.00 × 10⁻²), and SEPTIN3 (p = 4.00 × 10⁻²) (Figure 3H). SPTAN1 had the lowest p value in the screen and functions as an essential scaffold protein that stabilizes the plasma membrane and organizes intracellular organelles²⁴; therefore, we focused on its potential role in responding to shear stress.

To further study the role of SPTAN1 in mediating the response of non-adherent THP-1 cells to fluid shear stress, we used CRISPR to generate SPTAN1 knockdown (SPTAN1-KD) cells (Figure S8B). IL8 expression, cellular necrosis, and extracellular signal-regulated kinase (ERK)1/2 phosphorylation were used as readouts, since these markers have been shown to be activated by hemodynamic shear stress in previous work.⁸ qPCR showed a significant decrease in shear stress-induced IL8 expression in SPTAN1-KD as compared to control THP-1 cells (adjusted p = 0.0006; Figure 3I). In addition, flow cytometry after annexin V and propidium iodide staining indicated reduced cellular necrosis in sheared SPTAN1-KD THP-1 cells as compared to control cells (adjusted p < 0.0001; Figures 3J and S9A). Western blot analysis confirmed reduced ERK1/2 phosphorylation in SPTAN1-deficient cells (adjusted p < 0.0001; Figure 3K). Together, these results demonstrate that SPTAN1 plays a key role in mediating shear stress responses of non-adherent cells, which, to our knowledge, represents a non-canonical function for the SPTAN1 gene.

RAF1 mediates SPTAN1’s shear stress activation of non-adherent cells

SPTAN1 interacts with SRC kinase, a well-known tyrosine kinase involved in multiple signaling pathways, leading to activation of ERK signaling, which, in turn, promotes various biological processes.²⁵ However, inhibition of SRC signaling did not blunt the shear stress-induced activation of IL8 expression (Figure S8A) in THP-1 cells. This finding suggests that an alternative pathway is involved in SPTAN1 modulation of ERK activity in response to shear stress conditions. RAF1, a member of the mitogen-associated kinase (MAPK) pathway, was also identified as significant in the CRISPR screen (adjusted p = 7.88 × 10⁻¹⁵) (Figure 3F). The MAPK signaling pathways, including the RAS-RAF-MEK-ERK signaling cascades, regulate fundamental and diverse cell processes.²⁶ RAF1 activates MAP kinase-kinase (MEK), which, in turn, phosphorylates and activates MAPK, also known as ERK.^27,28 KRAS was examined, since it had the lowest p value (adjusted p = 0.06) of any of the RAS family members in our CRISPR screen. This signaling pathway prompted us to examine whether KRAS/RAF1 signaling is a mechanism by which SPTAN1 stimulates ERK activity in the context of shear stress. Using immunofluorescence (IF) and proximity ligation assays (PLA) approaches, we discovered that both KRAS and RAF1 co-localized with SPTAN1 (Figures 3L, 3M, and S8F). RAF1-KD cells (Figure S8C) were found to have decreased flow-mediated IL8 expression (adjusted p < 0.0001; Figure 3N) and necrosis (adjusted p < 0.0001; Figure 3O and S9B) as compared to wild-type cells. Shear stress-mediated phosphorylation of ERK was also decreased in RAF1-deficient cells, as determined by immunoblotting (adjusted p < 0.0001; Figure 3P). In summary, these discoveries suggest a novel interaction between KRAS/RAF1 and SPTAN1 that plays an important role in shear stress-mediated cytokine expression and necrosis.

Shear stress facilitates Ca²⁺ entry through a SPTAN1/RAF1 mechanism

In non-excitable cells, extracellular calcium can enter the cytoplasm through calcium release-activated calcium (CRAC) channels or through mechanosensitive ion channels, such as Piezo1 and transient receptor potential (TRP) channels, and this influx triggers downstream signaling pathways that regulate cell function and behavior.^29,30 Shear stress has been shown to drive inflammatory cytokine upregulation via calcium-dependent signaling pathways in monocytes.⁸ Furthermore, our analysis of DEGs from snRNA-seq revealed an enrichment of calcium-responsive genes at the end of CPB (Figure S10). However, there is a gap in understanding regarding the connection between calcium signaling and the SPTAN1/RAF1 mechanism in shear stress-induced inflammation. Using phosphoproteomics, we identified numerous phosphorylated sites on proteins in response to shear stress (Data S2). Among these phosphorylated proteins are members of the stromal interacting molecule (STIM) family (STIM1 and STIM2), which play a key role in SOCE. Upon activation, STIM1 and STIM2 migrate to the inner surface of the cell membrane, where they form complexes with the ORAI1 channel protein. ORAI1 forms a pore in the plasma membrane that drives calcium influx via its interaction with STIM1 or STIM2.³¹ Our proteomics results showed that STIM1 phosphorylation between amino acids 616 and 634 increased by over 80% after 30 min of shear stress exposure (p < 7.00 × 10⁻⁶⁰). STIM2, identified as a significant gene in our CRISPR screen (p = 5.98 × 10⁻⁶), presented an increase in phosphorylation between amino acids 717 and 730 of over 23% after 30 min of fluid stress exposure (p < 5.24 × 10⁻⁵⁸). The region where we found increased STIM1 phosphorylation has been shown to stimulate SOCE via an ERK1/2-dependent mechanism,³² thereby promoting calcium influx.

In order to determine whether SOCE is involved in shear stress-induced IL8 expression, we utilized a bimolecular fluorescence complementation (BiFC) STIM1/ORAI1 Venus reporter³³ in which the Venus signal develops when tagged STIM1 and ORAI1 are in close proximity. Sheared STIM1/ORAI1 Venus THP-1 cells showed an increased Venus signal (10.6% ±0.7 Venus positive) as compared to the static control (1.8% ±0.2) (adjusted p = 0.0002; Figures 4A and 4B). In addition, knocking down either STIM1 or ORAI1 (Figures S9D and S9E) significantly reduced responsiveness to hemodynamic stress compared to control cells, as measured by both IL8 expression and cell necrosis (adjusted p < 0.0001; Figures 4C, 4D, and S9C). Collectively, these results imply that STIM1 and ORAI1 are two key molecular mediators that promote IL8 expression upon exposure to shear stress in THP-1 cells.

Figure 4. Shear stress promotes calcium entry by activating SOCE via SPTAN1/RAF1 signaling.

(A) Schematic of the STIM1/ORAI1 complex using split Venus as a yellow signal reporter in a bimolecular fluorescence complementation (BiFC) assay. Yellow fluorescence indicates STIM1/ORAI1 interaction. The signal is higher after shear stress compared to the static condition in THP-1 cells.

(B) Quantification of the Venus-positive BiFC STIM1/ORAI1 THP-1 cells under shear stress and static conditions. The shear stress condition shows 10.60%-±0.70% Venus-positive cells, while the static condition only has 1.80% ±0.20% Venus-positive cells.

(C) STIM1-KD and ORAI1-KD cells have decreased shear stress-mediated IL8 expression as compared to control THP-1 cells, as quantified by qPCR. Replicates, n = 6.

(D) SPTAN1-KD and RAF1-KD cells have decreased STIM1 and ORAI1 interaction under shear stress condition based on decreased split Venus STIM1 and ORAI1 signal. Replicates, n = 6.

(E) Quantification of flow cytometry data, showing that STIM1-KD and ORAI1-KD cells have decreased shear-mediated cellular necrosis and increased cell survival. Replicates, n = 6.

(F) Proposed mode of the cascade molecular reaction induced by shear stress.

(G) FR180204, an ERK inhibitor, decreased shear stress-mediated IL8 expression in CD14⁺ primary monocytes.

(H) Quantification of flow cytometry data, displaying a decrease in shear stress-mediated necrotic cells following treatment with FR180204 in CD14⁺ primary monocytes.

Activated ERK1/2 is known to migrate to the cytosolic side of the ER and phosphorylate STIM1, enhancing STIM1’s ability to bind ORAI1 at the plasma membrane to form functional CRAC channels.^32,34 In shear stress-induced THP-1 cells, our results indicated that SPTAN1 and RAF1 colocalize with ERK1/2 and contribute to shear stress-mediated IL8 expression. However, the link between SPTAN1/RAF1 and the SOCE pathway remains unclear. Using the STIM1/ORAI1 Venus cells, we found SPTAN1 and RAF1-KD cells displayed a decreased shear-mediated Venus signal, suggesting that SPTAN1 and RAF1 play a role upstream of the SOCE pathway under shear stress conditions (24% reduction and 40.3% reduction, respectively) (Figure 4E). These results suggest a close linkage of hemodynamic shear stress activation of STIM1/ORAI1 with SPTAN1 and RAF1. Furthermore, our experiments indicate the significance of SOCE in regulating both flow-mediated gene expression and cell death.

Based upon this series of experiments involving SPTAN1 and RAF1 in THP-1 cells, we propose a mechanism whereby shear stress initiates ERK signaling via a mechanism that involves cell cortex proteins and KRAS/RAF1 signaling. The increased ERK1/2-mediated phosphorylation of STIM1, which couples to ORAI1, facilitates IL8 expression through enhanced Ca²⁺ entry.

Given our identification of RAF1-ERK1/2-SOCE as a critical driver of increased IL8 expression in THP-1 cells following exposure to shear stress (Figure 4F), we sought to further examine the effect of shear stress and shear-mediated ERK signaling on primary monocytes. We treated CD14⁺ primary human monocytes with FR180204, an ERK inhibitor, prior to exposing the cells to shear stress. Shear stress exposure is sufficient to upregulate IL8 expression (adjusted p < 0.0001; Figure 4G) and activate cellular necrosis (Figure S9D) in vehicle-treated CD14⁺ cells. Furthermore, shear stress-induced IL8 expression and cellular necrosis were significantly reduced in the sheared, FR180204-treated CD14⁺ primary human monocytes compared to vehicle control (adjusted p = 0.0046 and adjusted p < 0.0001; Figures 4G and 4H). These results demonstrate a similar regulatory gene pattern between THP-1 cells and CD14⁺ primary human monocytes, providing evidence that our findings are not limited to immortalized cell lines.

Candidate drugs inhibit shear stress-mediated IL8 gene expression

Since there are no pathway-specific drugs used to limit CPB-associated inflammation, and a recent randomized clinical trial has called into question the efficacy of perioperative corticosteroids in patients who underwent CPB,³⁵ we sought to test drugs that can target the shear stress-responsive pathways identified above. Three potential candidates were identified from FDA-approved drugs or compounds being studied in clinical trials. Sorafenib, a multi-kinase inhibitor drug³⁶ that can target RAF-1 signaling,³⁷ and ulixertinib (BVD-523), a selective inhibitor of the ERK1/2 pathway,³⁸ are FDA-approved molecules. Zegocractin (CM-4620), an ORAI1 blocker,³⁹ is currently being studied in clinical trials for COVID-19⁴⁰ and acute pancreatitis. To test whether these three drugs inhibit the shear stress-induced inflammatory cytokine expression, we performed scRNA-seq on PBMCs isolated from whole blood treated with these drugs prior to in vitro shear stress exposure. Vehicle-treated static and sheared samples were used as controls (Figure 5A). These experiments were performed with blood from three different donors. After quality control, 136,216 cells were retained and clustered into 11 distinct cell types (Figure 5B). Specifically, we generated feature maps to visualize IL8 expression in classical monocytes across five different conditions (Figure 5C). Similar to patterns identified in patients exposed to CPB and THP-1 cells, IL8 gene expression is significantly upregulated in classical monocytes under shear stress conditions compared to static ones (adjusted p = 3.75 × 10⁻⁶²; Figure 5D). Samples treated with the drug sorafenib showed elevated IL8 expression in classical monocytes compared to the shear stress condition (adjusted p = 0.01; Figure 5E). Conversely, both BVD-523 and CM-4620 significantly suppressed shear stress-mediated IL8 expression (p = 0.0001 and p = 1.16 × 10⁻¹⁸; Figures 5F and 5G). These findings suggest that drugs targeting the ERK1/2 and/or SOCE pathways could be potential candidates for managing inflammation during CPB. Additionally, we identified 295 and 173 DEGs in shear-stressed classical monocytes under BVD-523 and CM-4620 treatment, respectively, compared to the control. Functional enrichment analysis suggests that these genes are significantly enriched in cytokine stimuli and inflammatory response pathways (adjusted p < 0.05; Figures 5H and 5I), further supporting the potential of BVD-523 and CM-4620 to target shear stress-induced inflammation.

Figure 5. Drugs targeting the SPTAN1/RAF1/SOCE pathway modulate shear stress-mediated IL8 expression.

(A) Schematic of drug treatment experiments. Whole blood from healthy donors (n = 3) was incubated in 6-well suspension culture plates with 5 μM sorafenib, 1 μM BVD-523, 5 μM CM-4620, or DMSO control for 1 h prior to being sheared for 2 h. DMSO-treated blood samples under static conditions were used as controls. PBMCs were isolated for scRNA-seq.

(B) UMAP plot for cell clustering and cell type annotation of 136,216 PBMCs from 16 samples based upon scRNA-seq, with 11 distinct cell types annotated.

(C) Feature maps of IL8 expression in classical monocytes across five conditions: static, shear stress, sorafenib + shear, BVD-523 + shear, and CM-4620 + shear, respectively.

(D) Violin plot showing that IL8 expression was upregulated over 5.4-fold under shear stress conditions in classical monocytes compared to static conditions (adjusted p = 3.75 × 10⁻⁶²).

(E) Violin plot showing that the drug sorafenib upregulates IL8 expression in monocytes by 2-fold under shear stress conditions as compared to DMSO-sheared samples (adjusted p = 0.01).

(F and G) Violin plots show that BVD-523 and CM-4620 downregulated shear stress-induced IL8 expression in monocytes by 29.8% (adjusted p = 0.0001) and 35.4% (adjusted p = 1.16 × 10⁻¹⁸), respectively, as compared to DMSO sheared samples.

(H) Top 5 GO terms of functional enrichment results using 295 DEGs (adjusted p < 0.05) identified in classical monocytes with BVD-523 treatment compared to DMSO shear stress conditions.

(I) Top 5 GO terms of functional enrichment results using 173 DEGs (adjusted p < 0.05) identified in classical monocytes with CM-4620 treatment compared to DMSO shear stress conditions.

DISCUSSION

In this study, we used several approaches to address open questions related to how CPB instigates systemic inflammation, especially the contributions of the shear stress present in the CPB circuit. snRNA-seq data from neonatal patients exposed to CPB identified how CPB exposure modulates PBMC populations and the gene expression profiles within each cluster. snATAC-seq and in vitro ATAC-seq data demonstrate that CPB conditions are sufficient to alter chromatin accessibility. Through a genome-wide CRISPR screen and phosphoproteomics, we identified a novel pathway by which shear stress activates Ca²⁺ signaling in non-adherent cells. Targeting components of this pathway with clinically relevant small molecules reduced the shear activation of inflammatory gene expression in classical monocytes. These data may lead to focused interventions to reduce CPB-associated inflammation and other conditions in which leukocytes are exposed to supraphysiologic shear stresses, such as aortic or carotid stenosis. In addition, these findings advance our understanding of fundamental questions related to how cells “sense” shear stress.

There is growing interest in how biomechanical stimuli modulates leukocytes.⁴¹ We and other groups have found that shear stress can activate Ca²⁺ signaling in myeloid cells.^8,42,43 In contrast to previous candidate-based approaches,^42,43 which studied the contributions of PIEZO 1 and 2 to the shear stress response, we propose here a novel shear stress-responsive pathway consisting of SPTAN1/RAF1/SOCE/AP-1 signaling that is based on unbiased screens and confirmatory experiments. Our CRISPR interference screen and confirmatory experiments identify SPTAN1 and RAF1 as significant contributors in the shear activation of IL8 expression, cellular necrosis, and ERK1/2 phosphorylation. Immunofluorescence and proximity ligation data suggest that SPTAN1 colocalizes with RAF1, and we propose that this interaction is a key part of the shear stress-responsive pathway. One potential mechanism is that shear forces cause conformational changes in spectrin that promote KRAS/RAF1 interaction, thereby activating RAF1 and downstream ERK1/2 signaling. This premise is based upon data showing shear stress-induced conformational changes of red blood cell spectrin^11,44 and that SPTAN1 can act as a scaffold for protein kinases such as SRC.²⁵ Further experiments are needed to confirm this hypothesis.

Based upon our phosphoproteomic and BiFC data, we show that STIM1 and ORAI1 play important roles in the shear-mediated activation of Ca²⁺ signaling. The decreased STIM1/ORAI1 interaction in SPTAN1-KD and RAF1-KD cells (Figure 4D), along with published data showing that ERK1/2 mediated phosphorylation of STIM1³² promotes SOCE, suggest that SOCE is activated by SPTAN1 and RAF1. We further show here that shear stress can modulate chromatin accessibility, thereby influencing which TFs can bind to promotors to affect inflammatory pathways and cellular responses. Previous work from our lab reported that phosphorylated c-JUN (p-c-JUN) was activated in monocytes, resulting in inflammatory cytokine production in CPB.⁸ Since we have recently demonstrated that JUN, an AP-1 TF, translocates into the nucleus after shear stress via a Ca²⁺-dependent mechanism,⁸ the proposed mechanism has SOCE promoting AP-1 TF binding to chromatin, as demonstrated by the ATAC motif, DNA footprinting, and CUT&RUN data. Binding of AP-1 TFs may be responsible for shear stress-responsive gene expression. To our knowledge, our data are the first to suggest that SPTAN1 and RAF1 interact after shear stress, contributing to activate the pathway of calcium entry. More broadly, having the cell cortex GO term being a hit in the CRISPR screen leads to the premise that shear stress imposes loading on the cell cortex, which, in turn, activates downstream kinases that activate SOCE.

The single-nucleus sequencing data presented here provide the first comprehensive view of how PBMC populations change in response to CPB exposure, since, to our knowledge, this dataset is the first single-cell sequencing of leukocytes from CPB neonatal patients. Exposure to CPB modulated differential gene expression across all cell populations (Figures 1E–1G), and the DEGs in each cluster correlate to terms related to inflammation and activation of immune cells (Figure 1H). We focused on the classical monocyte cluster for deeper examination, given that it was the largest cluster at post-CPB time points (Figure 1C) and that previous studies have shown robust activation of monocytes during CPB.⁸ However, in addition to monocytes, our data show that B cells, CD8⁺ NKT-like cells, and effector/naive CD4⁺ T cells also exhibit a substantial number of DEGs and open chromatin accessibilities that should be further investigated. Our snRNA-seq data also show an increase in the relative size of the monocyte cluster after CPB exposure, a result that is consistent with published flow cytometry data.^45,46 While we have found significant shifts in the populations of PBMCs after CPB exposure, further work is necessary to elucidate the mechanisms that underpin these changes and the contributions of specific leukocyte populations.

The data presented in this paper will hopefully guide efforts to develop targeted treatments to limit CPB-associated inflammation. Currently, pediatric patients who underwent CPB are routinely administered preoperative steroids to limit inflammation. However, a recent large randomized clinical trial demonstrated that prophylactic use of steroids did not improve outcomes.³⁵ In our study, three of the four neonates received steroid treatment during CPB, with two treated before CPB and one after, which may have influenced the inflammatory gene expression responses observed in these patients. Importantly, our data identified and tested several therapeutic targets that have not been previously studied in the context of CPB. Specifically, we demonstrated that targeting ERK1/2 signaling with BVD-523 (ulixertinib) and ORAI1 with zegocractin (CM-4620) in an in vitro model of CPB significantly repressed the shear activation of inflammatory genes such as IL8 in classical monocytes. Paradoxically, our attempt to target RAF1 with sorafenib led to an increase in IL8 expression, which could be the result of sorafenib’s inhibitory effects on multiple kinases other than RAF1.³⁶ Additional work needs to be performed to determine whether ulixertinib, zegocractin, and/or other inhibitors of RAF1/ERK1/2/SOCE can be utilized to ameliorate CBP-associated inflammation in patients.

More broadly, the mechanistic experiments in this paper provide novel insight into how cells “sense” shear stress. In contrast to the extensive research looking at how shear modulates adherent cells like vascular endothelial cells, there is limited understanding of how non-adherent nucleated cells “sense” shear stress. In addition to the genes that we have validated in this paper and in previous work,⁸ it is worth noting that our CRISPR screen also identified CAV1, PECAM1, and VEGFR3. These genes have been shown to be involved in modulating the shear response of endothelial cells.^9,47 Although several candidate genes identified in our CRISPR screen have been validated, nearly 1,600 candidate CRISPR screen genes remain unstudied in this context and could be fertile ground to gain deeper insight into the mechanisms that underlie the shear stress response. Our whole genome CRISPR screen is one of the first efforts to use an unbiased method to identify genes critical for shear stress in PBMCs, with the sole other published effort looking at endothelial cells.⁴⁸ The shear-responsive pathway identified in this paper also has implications for other clinical situations where blood is exposed to increased shear stress, such as aortic valve and carotid artery stenoses, along with other situations where mechanical support of the circulation is required; e.g., ECMO and LVAD. In conclusion, we used single-cell profiling of neonatal PBMCs in conjunction with unbiased in vitro experiments to address important questions regarding the pathogenesis of CPB-associated inflammation along with how cells “sense” and respond to shear.

Limitations of the study

Several limitations of this study should be acknowledged. Since the patient data come from a relatively small population (four patients), follow-up studies with additional patients may identify additional DEGs and potential mechanisms, including sex-associated effects. Moreover, neutrophils were not studied in this report, since the snRNA/ATAC-seq workflow we used has limited efficiency for neutrophils. Future studies incorporating whole-blood or neutrophil-enriched single-cell sequencing will likely extend and complement the findings presented here. The in vitro drug studies have thus far focused on monocytes, and therefore the effects of these drugs on CPB-induced inflammation in other leukocyte populations is unknown. Further investigation will be necessary to fully elucidate the therapeutic potential of these drugs. We also acknowledge that supraphysiologic shear stress is not the only physiologic insult patients undergoing CPB experience, with loss of physiologic blood flow pulsatility during CPB, surgical trauma, and ischemia-reperfusion injury being some of the other major insults. Overall, there are a number of open and important questions remaining that warrant additional work to improve outcomes for patients undergoing CPB.

RESOURCE AVAILABILITY

Lead contact

Requests for further information and resources should be directed to Vishal Nigam (vishal.nigam@seattlechildrens.org).

Materials availability

All materials and reagents generated in this article are available upon request from the lead contact. Detailed protocols for experiments reported in this article are available upon request from the lead contact.

Data and code availability

• The snRNA-seq, snATAC, bulk RNA-seq, bulk ATAC-seq, and CUT&RUN datasets generated in this study have been deposited in the GEO and made publicly available. Accession numbers are listed in the key resources table.

• No original code was generated with this study.

• Any additional information needed to reanalyze the data is available upon request from the lead contact.

STAR★METHODS

KEY RESOURCES TABLE

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Neonatal patient PBMC collection

Human subject use in this study was approved by the Institutional Review Board of the Seattle Children’s Hospital (Study00001118). Pediatric patients less than 1 month old with different congenital heart defects requiring repair utilizing CPB were enrolled in our study at Seattle Children’s Hospital (Seattle City, Washington, USA). Written informed consent was received from participants’ parents or legal guardians prior to inclusion in the study. A standard CPB protocol was utilized in all the patients. Approach in all cases was via median sternotomy. Aortic cannulation was used for the arterial access, with either single venous cannula being placed in the right atrium or bicaval cannulation, depending on the type of the defect requiring repair. After initiation of CPB, the patients were cooled to 18°C–30°C. Upon completion of repair, the patients were rewarmed and weaned from CPB. MUF was performed in all the patients after weaning from CPB. Demographic and clinical characteristics of the patient participants including age, sex, lesion type, CPB time, aortic cross-clamp time, peak lactate, time to extubation, and PODs in the CVICU, are summarized in Table S1. Patient samples were grouped by time points as defined in the study design and no randomization was performed.

Cell lines

All cell culture procedures were conducted under BSL-2 conditions. THP-1 human monocytic leukimia cells (ATCC) used for CRISPR screens and in vitro CPB shear stress experiments were cultured in RPMI-1640 media supplemented with 10% FBS (Gibco), 50 U/mL penicillin, and 50 μg/mL streptomycin (Gibco) in 5% CO2 at 37°C. 293T cells (Takara) used for lentiviral construct generation were cultured in DMEM with 10% FBS, 50 U/mL penicillin, and 50 μg/mL streptomycin (Gibco) in 5% CO2 atmosphere at 37°C. Cell lines were authenticated by DNA sequencing and were periodically tested and confirmed to be free of mycoplasma contamination.

Primary human CD14⁺ cell experiments

Human CD14⁺ primary monocytes were purchased from Bloodworks NW (Seattle, WA). After thawing, dead cells were removed using the Miltenyi Biotec dead cell removal kit (130–090-101). The cells were either treated 30 μM FR180204 (Cayman) or DMSO prior to the shear stress experiments.

METHOD DETAILS

Patient PBMCs isolation

Blood samples of 2.5 mL were collected from an indwelling patient line or from the CPB pump, dependent upon the time of the blood draw (for blood draws performed while the patient was on CPB, these samples were pulled from the CPB pump). These samples were collected in EDTA tubes at 4 time points: Pre (before CPB), Post (after CPB), 8h, and 24h during postoperative recovery. PBMCs were isolated using the Stemcell EasySep Direct Human PBMC Isolation kit.

snRNA-seq and snATAC-seq of PBMCs from patients

Single-nuclei multiome ATAC + gene expression sequencing

The Chromium Next GEM Single-cell Multiome ATAC + Gene expression (GEX) protocol (CGOOO338, https://cdn.10xgenomics.com/image/upload/v1666737555/support-documents/CG000338_ChromiumNextGEM_Multiome_ATAC_GEX_User_Guide_RevF.pdf) was used to process single nuclei sequencing in PBMCs. For cell preparation, cryovials of patient PBMCs were removed from storage and thawed in a 37°C water bath for 1–2 min. The cells are removed from the water bath once only a small ice crystal remains. The thawed cells are then moved into a conical tube (50 mL). The cryovial is rinsed with pre-warmed growth media (1 mL of RPMI +10% FBS), and this rinse is added gradually to the conical tube while the tube is kept gently sharked (300 rpm at 37°C on the thermomixer). Sequentially dilute cells in the 50 mL conical tube by incremental 1:1 volume additions of media for a total of 5 times, with ~1 min wait between additions. Then add again the media (RPMI +10% FBS) at a speed of 1mL/3~5 s to the tube and swirl. Centrifuge at 300 rcf for 5 min. Remove most of the supernatant, leaving ~1 mL and resuspend cell pellet in this volume. Add an additional 9 mL media (1 mL/3–5 s) to achieve a total volume of ~10 mL. Centrifuge at 300 rcf for 5 min. Remove the supernatant without disrupting the cell pellet and resuspend in 1 mL PBS +0.04% BSA, gently pipette mix 5x. Transfer to a 2 mL microcentrifuge tube. Rinse the 50 mL tube with 0.5 mL PBS +0.04% BSA and transfer the rinse to the 2 mL tube containing the cells. Mix by gently inverting the tube. Centrifuge cells at 300 rcf for 5 min. The remaining liquid is removed while keeping the pellet intact, after which the pellet is resuspended in buffer containing 1 mL PBS and 0.04% BSA. The resulting suspension is then clarified by passing it through a fine (40 μm) cell-straining device. Following the previous steps, the nuclei isolation procedure continues by adding 100,000–1,000,000 cells to a 2 mL microcentrifuge tube, centrifuging at 300 rcf for 5 min at 4°C, removing all supernatant, adding 100 μL chilled Lysis Buffer and pipette mix 10x, then incubating on ice for 3–5 min. Next, add 1 mL chilled Wash Buffer to the lysed cells, pipette mix 5x, and centrifuge at 500 rcf for 5 min at 4°C. Carefully remove the supernatant without disturbing the nuclei pellet, and repeat the wash steps two more times for a total of three washes.

Single-nuclei sequencing data preprocessing

Single-nuclei RNA sequencing data processing and analysis started with raw data from PBMCs generated via the 10x Genomic Chromium platform. ‘cellranger-arc’ (version: 2.0.2) and ‘cellranger’ (version: 7.1.0) software were used to perform read alignment, low-quality read filtration, barcode counting, and UMI tallying, consistently applying default parameters.

snRNA-seq

Using the raw count matrix obtained from Cellranger’s output, we employed a single-cell analysis workflow via Seurat (version: 4.3.0.1) to characterize and visualize biologically significant cell populations.⁴⁹ The analysis pipeline consists of critical procedures: data loading and preprocessing, dimensionality reduction, clustering, cell type annotation and DEG identification. During data loading and preprocessing stage, the process begins with creating a Seurat object (Parameters: min.cells = 3 and min.features = 200). Then, cells with a high percentage of mitochondrial (over 20) and doublets were excluded. In the process of integration, the ‘FindIntegrationAnchors’ function identified integration anchors, and then ‘IntegrateData’ function integrated and harmonized data across all patient samples. The scaling of the integrated assay was accomplished with the ‘ScaleData’ function, ensuring consistent gene expression profiles across samples. Principal Component Analysis (PCA) was utilized to reduce the dimensionality of the integrated assay while retaining the most informative features (genes). t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) techniques were employed for visualization purposes. The ‘FindNeighbors’ and ‘FindClusters’ functions were applied to perform cell clustering within the dimensionality-reduced space derived from PCA based on gene expression profiles. Identification of marker genes for each cell cluster is accomplished using the ‘FindAllMarkers’ function, which assists in annotating clusters according to marker gene expression profiles or known cell type-specific markers. Cell type annotation was initially performed using ScType and subsequently refined manually. Differential gene expression analysis was achieved by ‘FindMarkers’ function between different stages with the parameter log2FC.threshold = 0.1. The adjusted p value less than 0.05 was considered statistically significant.

snATAC-seq

snATAC-seq data was mainly processed with ArchR (version: 1.0.2) and Signac (version: 1.10.9001).^50,51 We initiated the process of snATAC-seq data using ‘createArrowFiles’ function (Parameters: minTSS = 4, minFrags = 1000) in ArchR via ATAC fragments files generated by cellranger-arc above. Low quality cells and doublets were filtered. Based upon the high-quality cells, we performed dimensionality reduction with snATAC using the approach Latent Semantic Indexing (LSI) in ArchR. We then identified the cell clusters through ‘addclusters’ function with resolution at 0.2. The single-cell embeddings UMAP and t-SNE were applied to visualize the single-cells in the reduced dimension space through LSI. Marker features of snATAC was obtained via ‘getMarkerFeatures’ function in ArchR with Adjusted P ≤ 0.01 and Log2FC ≥ 1.25 as cutoff.

To identify differential accessible peaks (DAPs) using Signac between four stages, the ArchRProject was converted to a Signac SeuratObject via ArchRtoSignac.⁵² Then we conducted DAPs analysis using ‘FindMarkers’ function in Signac with the parameter test.use = ‘LR’. A p value less than 0.05 is considered statistically significant.

Transcription factor (TF) motif enrichment analysis for DAPs was performed through ‘peakAnnoEnrichment’ function in ArchR. TF footprinting was computed via ‘getFootprints’ in ArchR.

Merge snRNA and snATAC

The cell annotation of snATAC clusters were labeled with the annotation names from snRNA-seq data. We manually checked and rectified the cell types according to the gene scores of markers detected in snATAC.

scRNA-seq of PBMCs in drug treatment experiments

Whole blood from healthy donors (n = 3) was incubated in 6-well suspension culture plates with either 5 μM sorafenib, 1 μM BVD-523, 5 μM CM-4620, or DMSO control for 1 h at 37°C and 5% CO₂. The DMSO concentration in each condition was 0.02%(v/v). Static control samples were incubated at 37°C and 5% CO₂ for an additional 2 h. For shear conditions, ~3 mL blood was transferred to a pump circuit consisting of 11 ft of PVC tubing (Tygon Lab E–3603 L/S 13) and circulated by a peristaltic pump (MasterFlex) at 10 mL/min for 2 h. The temperature was maintained at 30°C by submerging the tubing in an external water bath. Shear samples and static control samples were then fixed with 4% PFA at 4°C overnight (~20 h) as per 10x Genomics Demonstrated Protocol CG000721revB. Glycerol was added to a final concentration of 5% (v/v) and samples were stored at −80°C until PBMC isolation. PFA-fixed whole blood samples were thawed at room temperature and PBMC’s were isolated by negative magnetic selection (StemCell Technologies). Gene expression was examined using single-cell RNA profiling with the Chromium Next GEM Fixed RNA Profiling kit (10x Genomics). 6,000 cells/sample were targeted for RNA profiling and cDNA libraries were sequenced on a NextSeq 2000 sequencer using a single XLeap P4 flow cell (Illumina).

Raw datasets were processed, including read alignment, low-quality read filtration, barcode counting, and UMI tallying, using CellRanger with the ‘multi’ parameter (version: 7.0.0). After obtaining the count matrix, the data processing work flow was the same as the pipeline described in the “snRNA-seq” section.

Single-cell visualizing

For the single-cell visualizing, we used the R package ‘dittoSeq’⁵³ and its Python-based counterpart ‘Scanpy’⁵⁴ to visualize cell clustering results and cell type annotations, to generate heatmaps, feature maps, and dot plots for markers of cell type, DEGs, and DAPs. Moreover, tailored R and Python scripts were utilized to produce diverse graphical representations, such as bar graphs for visualizing cell fraction data, dot plots for displaying the functional enrichment results of DEGs.

Bulk RNA-seq of THP-1 cells

Raw reads from static and sheared THP-1 were quality controlled with trim_galore (version: 0.6.10). High-quality reads were mapped to GRCh38 human genome with STAR (version: 2.7.11b)⁵⁵ aligner. FeatureCounts (version: v2.0.6) was employed to count mapped reads for each transcript.⁵⁶ Differential expression genes were identified using DESeq2 (version: 1.38.3).⁵⁷

Cloning, cell culture, and viral transfection

Generation of IL8-GFP reporter construct

pCDH-CMV-Nluc (a gift from Kazuhiro Oka, Addgene 73038) was cut with ClaI and BamHI to remove CMV enhancer and promoter. Human IL8 (hIL8) promoter was PCR cloned from genomic DNA using primers CTCATCGAT GTAACGTGATCACTGCCATGT and CTCGGATCC AGTGCTCCGGTGGCTTTTTA and ligated into the ClaI and BamHI sites of the pCDH backbone. Enhanced GFP (EGFP) was PCR cloned from pLenti CMV GFP Puro (Addgene 658–5) with primers CTCGTCGACCATGGTGAGCAAGGG and CTCGGATCC CGTTACTTGTACAGCTCGT and cloned into hIL8-pCHD at the SalI and BamHI restriction sites.

Cloning for bimolecular fluorescence complementation (BiFC) of store operated calcium entry (SOCE) genes lentivirus constructs: N-terminal Venus’s fragment fused to full-length ORAI1 and C-terminal Venus fused to full-length STIM1 were cut out and amplified from plasmids pcDNA3-Venus-173-N-ORAI1 (a gift from Jin Zhang; Addgene plasmid #87618) and pcDNA3.1-STIM1-Venus-173-C (a gift from Jin Zhang; Addgene plasmid #87619), respectively. These two inserts were separately cloned into the pCDH-CMV-Nluc plasmid (a gift from Kazuhiro Oka; Addgene plasmid #73038) in place of the Nanoluciferase (Nluc) reporter using BamH1 and EcoR1 restriction sites to yield two distinct plasmids, each containing a unique fragment for BiFC. These plasmids were used for lentiviral transfection in 293T cells, and viral supernatant containing the lenti-plasmids with V173N-ORAI1 and STIM1-V173C fragments were transduced into one sample of THP-1 cells at multiplicity of infection of 10.

Cell culture and viral transfection

Human monocytic cells THP-1 (ATCC) were cultured in RPMI-1640 media supplemented with 10% FBS (Gibco), 50 U/mL penicillin, and 50 μg/mL streptomycin (Gibco) in 5% CO2 at 37°C. Human embryonic kidney cells Lenti-X 293T (Clontech) were cultured in DMEM media supplemented with 10% FBS (Gibco), 50 U/mL penicillin, and 50 μg/mL streptomycin (Gibco) in 5% CO2 at 37°C. pCDH-hIL8-EGFP plasmid was transfected into 293T cells using TransIT-Lenti (Mirus Bio). Viral supernatants were collected at 48 h after transfection and transduced in THP-1 cells.

Exposure to hemodynamic shear stress

For the in vitro hemodynamic stress model, monocytes at a density of 2 million cells/mL were sheared for either 2hrs or 30min in a 10ft-long Masterflex Tygon E–3603 L/S13 pump tubing (Cole-Parmer), using the Masterflex miniflex pump model 115/230 VAC 07525–20 (Cole-Parmer) at 10 mL/min. The average shear stress on cells from the roller pump is estimated to be 2.1 Pa. More than three-quarters of the tubing was submerged in the water bath for temperature control at 30°C.

Genome-wide CRISPR screen in THP-1 cells

CRISPR screen and in vitro CPB shear treatment

Wild-type and IL8-GFP THP-1 cells were transduced with a lentiviral preparation of the Brunello gRNA pooled library in lenti-CRISPRv2 (a gift from David Root and John Doench, Addgene #73179-LV) at multiplicity of infection (MOI) of 1. Cells were puromycin selected after 4 days post transduction and recovered for another 2 days. Then, sheared in an in vitro CPB system⁸ at 2 million cells/mL at a rate of 10mL/min for 2 h. After shear, cells were prepared for flow cytometry and fluorescence-activated cell sorting (FACS).

IL8 flow cytometry

On the day of shear treatment, transduced THP-1 cells were pre-treated with eBioscience Protein Transport Inhibitor Cocktail 500x (Thermo 00–4980-93) at 10μL/mL for 30 min. Sheared Brunello library transduced and untransduced THP-1 along with static control THP-1 cells were washed in 1.5mL PBS, fixed in 1mL IC Fixation Buffer (Thermo 00–8222-49) and incubated at 20 min in the dark at room temperature, with gentle agitation. Cells were permeabilized in 1.5mL 1x Permeabilization Buffer (00–8333-56), incubated at room temperature for 10 min and spun at 500 × g for 5 min. Directly conjugated IL8 antibody (Invitrogen, 12808842) was incubated with THP-1 cells in 1x Permeabilization Buffer for 45 min in the dark. Cells were then washed in more 1x Permeabilization Buffer and resuspended in Flow Cytometry Staining Buffer (Thermo 00–4222-26) at 10 million cells/mL for flow cytometry analysis.

IL8-GFP FACS

Sheared Brunello library transduced THP-1 cells were sorted using FACS analysis (BD FACSAria) for GFP intensity. Cells were sorted into two groups, the top 10% intensity and the next or subsequent 25% intensity. Transduced but non-sheared cells were fixed and stained using the same protocol to serve as the baseline.

Genomic DNA Extraction and next-generation sequencing

Sorted and fixed cells were spun at 300g for 3 min. Proteinase K (Zymo) was added at 5uL and incubated at 55C for 2 h. Samples were then de-crosslinked by incubation at 94C for 20 min and subsequently lysed and extracted for genomic DNA (gDNA) using the Quick-DNA/RNA Miniprep Kit (Zymo D7005T). gDNA samples of both samples were sent to Cellecta Inc. for next-generation sequencing, using Illumina NextSeq.

Genome-wide CRISPR data analysis

The R package MAGeCKFlute was used to analyze the sequencing data for gRNAs that were enriched.⁵⁸ Data from the 10% brightest cell population were compared to the data from the next 25% brightest and unsheared cell populations.

The candidate genes underrepresented in the 10% brightest pool with an adjusted p < 0.05 were inputted into Metascape to identify enriched GO-terms.

CRISPR/Cas9 mediated gene knockdown

Lentiviral vectors together with helper plasmids encoding gag, pol and rev, were co-transfected in 293T cells using TransIT-Lenti (Mirus Bio). Viral supernatants were collected at 48 h and 72 h after transfection and centrifuged at 1,000 g for 10 min. THP-1 cells were then transduced at the multiplicity of infection of 10 using the viral supernatants supplemented with 5 μg/mL polybrene for 6 h. Three days after transduction, positive cells were selected in 1 μg/mL puromycin for 72 h, if necessary. Gene knockdowns for SPTAN1 and Raf1 were also transduced into THP-1 cell lines containing the Venus BiFC plasmids and studied for shear stress experiments. Raf1 gRNA sequence derived from a 3^rd generation lentiviral gRNA plasmid targeting human Raf1 (A gift from John Doench, David Root, Addgene plasmid #76708).

Specifically, for CRISPR/Cas9-mediated gene knockout, we first generated a doxycycline-inducible Cas9-expressing THP-1 cell line (iCas9-expressing cells) by transducing THP-1 cells with lentiviruses carrying the Lenti-iCas9-neo plasmid (a gift from Qin Yan; Addgene plasmid #85400). Multiple guide RNAs targeting a gene were cloned into the Lenti-multi-Guide plasmid (a gift from Qin Yan; Addgene plasmid #85401) and delivered to iCas9-expressing THP-1 cells by the lentiviral system. gRNA sequences can be found in Table S3. Cells were then treated with 1 μg/mL of doxycycline for 48 h to induce editing at the targeted loci.

For IL8 gene expression analysis by qPCR and pERK/ERK western blotting, THP-1 knockdown samples were sheared for 2hrs and collected immediately for RNA and total lysate. For cell death assaying, THP-1 knockdown samples were sheared for 2 h and then rested in 37°C for 24 h, and then prepped for flow cytometry analysis. For ICC/IF and PLA analysis, wild type THP-1 cells were sheared for 2 h and then prepped onto microscope slides. For Venus BiFC analysis, THP-1 cells containing Venus BiFC of ORAI1 and STIM1 were sheared for 30 min, rested at 37°C for 24 h, and then prepped for confocal microscopy imaging and flow cytometry analysis. Venus BiFC THP-1 cell lines with SPTAN1 and Raf1 knockdowns were sheared for 2 h, rested at 37°C for 24 h, and then prepped for flow cytometry analysis.

Flow cytometry and cell death assays

For the cell death assays, THP-1 cells were stained with Alexa Fluor 488 annexin V and propidium iodide according to the manufacturer’s protocol for the Dead cell apoptosis kit (V13241, Thermo Fisher). Samples were immediately analyzed by flow cytometry.

For flow cytometry on samples containing Venus BiFC, samples were collected and fixed in 2% paraformaldehyde, and then washed and suspended in PBS for flow cytometry analysis using the BD LSR II Flow Cytometer. An Alexa 488 laser was used for excitation of the cells, and the band-pass filter of 530/30nm was used to capture the emission of Venus fluorescence.

Quantitative analysis and gating were performed on all flow cytometry experiments using FlowJo software.

Immunocytochemistry/Immunofluorescence (ICC/IF)

THP-1 cells were fixed in 2% paraformaldehyde for 10 min at room temperature, centrifuged at 300×g for 5 min and washed with 1 mL of PBS. Cells were suspended in 20 μL of PBS, spread on microscope slides, let air-dried and rinsed with water before being stored at 4°C. When ready for ICC/IF procedures, fixed cells were permeabilized with 0.1% Triton X-100 made in PBS solution. ICC/IF analyses were performed using primary antibodies against SPTAN1 (MAB1662), k-Ras (AB108602), and Raf1 (AB137435) and secondary antibodies Alexa 488 (A32723 mouse, Life Technologies) and Alexa 594 (A32740 rabbit, Life Technologies). Images were taken using confocal microscopy techniques.

Duolink proximity ligation assay (PLA)

Protein-protein interactions (PPI) of SPTAN1-kRas and SPTAN1-Raf1 were identified using the Duolink^® proximity ligation assay (PLA) in situ red for mouse/rabbit antibodies (DUO92101, Sigma). THP-1 cells were fixed onto microscope slides and permeabilized as outlined in the ICC/IF section. Fixed THP-1 cells were incubated with two primary antibodies for each PPI, SPTAN1 (MAB1662 mouse, Sigma) and k-Ras (AB108602 rabbit, Abcam), and SPTAN1 (MAB1662 mouse, Sigma) and Raf1 (AB137435 rabbit, Abcam) in a dark box overnight in 4°C. After primary incubation and PBS wash, the samples were incubated with secondary antibodies conjugated with PLA probe oligonucleotides (PLA PLUS/MINUS). Subsequently, a ligation solution containing hybridizing connector oligos and ligase enzyme was added to join the two PLA probes in close proximity (<40nm). Lastly, an amplification solution was added containing a DNA polymerase and red fluorescently labeled complementary oligonucleotides to create the red fluorescent puncta signal detected and imaged by confocal microscopy.

Phospho-proteomics

Sheared and static wild-type THP-1 cells were pelleted and resuspended in PBS with protease and phosphatase inhibitor.

Protein digestion and sample preparation for mass spectrometry

3 mLs of 6 Molar Guanidine solution was added to cell pellet and mixed. The samples were then boiled for 5 min followed by 5 min cooling at room temperature. The boiling and cooling cycle was repeated a total of 3 cycles. The proteins were precipitated with addition of methanol to final volume of 90% followed by vortex and centrifugation at maximum speed on a benchtop microfuge (4000 rpm) for 20 min. The soluble fraction was removed by flipping the tube onto an absorbent surface and tapping to remove any liquid. The pellet was suspended in 4mL of 8 M Urea made in 100mM Ammonium Bicarbonate. TCEP was added to final concentration of 10 mM and Chloro-acetamide solution was added to final concentration of 40 mM and vortex for 5 min 3 volumes of 50mM ammonium bicarbonate were added to the sample to reduce the final urea concentration to 2 M. Trypsin was in 1:50 ratio of trypsin and incubated at 37C for 48 h. The solution was then acidified using TFA (0.5% TFA final concentration) and mixed. Samples were desalted using 100 mg C18-SPR (waters) as described by the manufacturer protocol. The peptide concentration of sample was measured using BCA. 100 μg of each were then labeled using TMT10 (as suggested in manufacturer protocol, Thermo) for one hour and quenched using hydroxylamine. The samples were then pooled and dried.

High-Select Fe-NTA Phosphopeptide Enrichment (A32992 Thermo Scientific): 1 mg of total peptide is used following manufacturer’s protocol.

LC-MS-MS: 1 μg of total enriched phospho-peptides were analyzed by ultra-high pressure liquid chromatography (UPLC) coupled with tandem mass spectroscopy (LC-MS/MS) using nano-spray ionization. The nanospray ionization experiments were performed using a Orbitrap fusion Lumos hybrid mass spectrometer (Thermo) interfaced with nano-scale reversed-phase UPLC (Thermo Dionex UltiMate 3000 RSLC nano System) using a 25 cm, 75-μm ID glass capillary packed with 1.7-μm C18 (130) BEH beads (Waters corporation). Peptides were eluted from the C18 column into the mass spectrometer using a linear gradient (5–80%) of ACN (Acetonitrile) at a flow rate of 395 μL/min for 3h. The buffers used to create the ACN gradient were Buffer A (98% H₂O, 2% ACN, 0.1% formic acid) and Buffer B (100% ACN, 0.1% formic acid). Mass spectrometer parameters are as follows; an MS1 survey scan using the orbitrap detector (mass range (m/z): 400–1500 (using quadrupole isolation), 120000 resolution setting, spray voltage of 2200 V, Ion transfer tube temperature of 275 C, AGC target of 400000, and maximum injection time of 50 ms) was followed by data dependent scans (top speed for most intense ions, with charge state set to only include +2–5 ions, and 5 s exclusion time, while selecting ions with minimal intensities of 50000 at in which the collision event was carried out in the high energy collision cell (HCD Collision Energy of 33%), and the fragment masses where analyzed in the ion trap mass analyzer (With ion trap scan rate of turbo, first mass m/z was 100, AGC Target 5000 and maximum injection time of 35ms). Protein identification and label free quantification was carried out using Peaks Studio 8.5 (Bioinformatics solutions Inc.).

PP1 inhibitor experiment

Human monocytic cells THP-1 (ATCC) were cultured in RPMI-1640 media supplemented with 10% FBS (Gibco), 50 U/mL penicillin, and 50 μg/mL streptomycin (Gibco) in 5% CO₂ at 37°C. Human embryonic kidney cells Lenti-X 293T (Clontech) were cultured in DMEM media supplemented with 10% FBS (Gibco), 50 U/mL penicillin, and 50 μg/mL streptomycin (Gibco) in 5% CO₂ at 37°C.

For experiments studying the effect of SRC-selective tyrosine kinase inhibitor PP1 (5678091MG, MilliporeSigma), THP-1 cells were pre-incubated with 25μM PP1 inhibitor or vehicle (DMSO) for 30 min before shearing for 2 h.

RNA isolation and quantitative PCR

Static and sheared cells were collected at a density of 2 million cells/mL, spun at 300 g × 3 mins, and lysed in 350μL RLT buffer from RNeasy Mini Kit (74104, Qiagen). RNA was then isolated using the manufacturer’s protocol for the RNeasy Mini Kit column purification. cDNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (4368814, Thermo Fisher). Gene expression of IL8 was quantified using SYBR Green detection method (primer sequence in Table S2). All expression data were normalized to internal control genes, 18 S ribosomal RNA (RN18S1) or glyceraldehyde 3-phosphate dehydrogenase (GAPDH) for human samples. Relative quantification of fold-change was performed using the 2−Δ/ΔCt method.

Western Blot

Static and sheared cells were collected at a density of 2 million cells/mL, spun at 300 g × 3mins, and lysed in 100uL of RIPA (89900, Thermo Fisher). Total protein concentrations in cell lysates were measured using the Pierce bicinchoninic acid (BCA) assay kit (23225, Thermo Fisher). 10–15 μg of proteins were separated by SDS-PAGE and immunoblotted using the following antibodies: p-ERK1/2 (9101, Cell Signaling), ERK1/2 (4695, Cell Signaling), SPTAN1 (MAB1622, Sigma), Raf1 (AB137435, Abcam), and the loading control β-actin ACTB (SC-47778, Santa Cruz). Co-detection was performed using IRDye 700 and 800-labeled secondary antibodies (926–68070 and 926–32211, Li-Cor) under the Odyssey infrared laser fluorescence scanner (Li-Cor) and quantified using ImageJ.

ATAC-seq and CUT&RUN in THP-1 cells

ATAC-seq

For Flash-frozen tissue was sent to Active Motif to perform the ATAC-seq assay. The tissue was manually disassociated, isolated nuclei were quantified using a hemocytometer, and 100,000 nuclei were tagmented as previously described,⁵⁹ with some modifications based on⁶⁰ using the enzyme and buffer provided in the Nextera Library Prep Kit (Illumina). Tagmented DNA was then purified using the MinElute PCR purification kit (Qiagen), amplified with 10 cycles of PCR, and purified using Agencourt AMPure SPRI beads (Beckman Coulter). Resulting material was quantified using the KAPA Library Quantification Kit for Illumina platforms (KAPA Biosystems) and sequenced with PE42 sequencing on the NextSeq 500 sequencer (Illumina).

CUT&RUN

The CUTANA CUT&RUN Library Prep Kit was utilized to process CUT&RUN experiments in THP-1 cells. It includes eight basic steps. Step 1: Immobilize cells. Cells are bound to magnetic beads coated with Concanavalin A (ConA), a lectin that binds to cell surface proteins. This supports high-throughput formatting and simplifies the separation of cells from on-target chromatin in Step 6. Step 2: Permeabilize cells. Immobilized cells are treated with a buffer containing digitonin, a nonionic detergent that permeabilizes cell membranes at low concentrations. Step 3: Incubation with c-JUN antibody. c-JUN antibody to the target of interest is added to the reaction and incubated overnight at 4°C. The negative control (e.g., IgG) and the positive control (e.g., H3K4me3, H3K27me3) were also performed in this experiment. Step 4: Add pAG-MNase. The following day, bead-bound cells are washed and then pAG-MNase is added to the reaction. The immunoglobulin binding properties of pAG act to “tether” MNase to antibody-bound chromatin. To prevent nonspecific cleavage, the cell/bead mixture is washed several times following pAG-MNase incubation. Step 5: pAG-MNase Activation. Calcium (Ca²⁺ is added to the reaction to activate MNase, which cleaves DNA proximal to where the antibody is bound. Cleaved chromatin fragments diffuse into the supernatant, while remaining bulk chromatin remains inside the bead-immobilized cells. Step 6: DNA purification. Isolation of CUT&RUN enriched DNA is straightforward since the cells remain bound to magnetic ConA beads. Bead-coupled cells containing bulk chromatin are magnetically separated from the clipped target DNA, which remains in solution. Target DNA is purified using a column clean up kit optimized for small fragments and quantified with a fluorometric assay (ThermoFisher Qubit). Step 7: CUT&RUN library prep. Purified CUT&RUN DNA is repaired, ligated to sequencing adapters, and PCR-amplified to generate NGS libraries. PCR is performed using parameters optimized for low CUT&RUN yields and small fragment sizes, and barcoded primers are used to enable multiplexed sequencing. Step 8: Next-generation sequencing (NGS). Libraries are pooled at equimolar ratios and loaded onto the desired platform for NGS.

Analysis of ATAC-seq and CUT&RUN

Quality control and adapter trimming on raw data from THP-1 cells were conducted using trim_galore (version: 0.6.10). High-quality reads were mapped to the hg38 human reference genome using Bowtie2 (version: 2.5.2) with parameters -very-sensitive -X 2000. Duplicate reads were marked using picard (version: 2.27.5). Peaks calling was implemented via macs2 (version: 2.2.9.1). The intersect peaks of replicates on each condition were identified using bedtools (version: v2.31.0). The consensus peaks between control and experiment were recognized through bedops (version: 2.4.41). FeatureCounts (version: v2.0.6) was used to count mapped reads for each peak. Differential accessible peaks were achieved using edgeR (version: 3.40.2). The adjusted p less than 0.01 was considered statistically significant. The structure annotation of peaks was performed using ChIPseeker (version: 1.34.1). Quality control report of ATAC-seq data was shown in Figure S11.

Functional enrichment analysis

Functional enrichment analysis for candidate genes was conducted using Metascape.⁶¹ Statistical significance was acknowledged when the P-value was less than 0.01.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analyses of snRNA-seq, scRNA-seq, bulk RNA-seq, ATAC-seq, and CUT&RUN

Differential expression analyses of snRNA-seq and scRNA-seq used two-sided Wilcoxon rank-sum tests with Benjamini-Hochberg FDR correction. The DESeq2 used in bulk RNA-seq analysis employed a negative binomial model with the Benjamini-Hochberg procedure to compute adjusted p-values. The adjusted p value less than 0.05 was considered statistically significant. The edgeR used in ATAC-seq and CUT&RUN also applied Benjamini-Hochberg method to calculate adjusted p-values. The adjusted P in ATAC-seq and CUT&RUN less than 0.01 was considered statistically significant.

Statistical methods for other in vitro experiments

All statistical analyses were performed using Prism 8 (GraphPad). Numeric differences between 2 groups were compared using a 2-tailed Student’s t test. Comparisons of experimental groups against a control group (e.g., CTR Shear 2h) were performed using 1-way ANOVA and post hoc Dunnett’s test. Other pairwise comparisons for more than 2 groups were performed using 1-way ANOVA and post hoc Tukey’s test. p < 0.05 was considered significant. Data are represented as mean ± SEM.

Supplementary Material

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2025.116903.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Chemicals and peptides
RPMI-1640	Gibco	Cat# 11875093
Fetal Bovine Serum, heat inactivated	Gibco	Cat# 16140-071
Penicillin/Streptomycin	Gibco	Cat# 15140122
DMEM	Gibco	Cat# 10566016
PBS	Gibco	Cat# 10010023
TransIT-Lenti Transfection Reagent	Mirus Bio	Cat# MIR 6603
FR180204 (ERK Inhibitor II)	Cayman	Cat# 15544
DMSO	Alfa Aesar	Cat# 42780
eBioscience™ Protein Transport Inhibitor Cocktail	Thermo Fisher	Cat# 00-4980-93
IC Fixation Buffer	Thermo Fisher	Cat# 00-8222-49
Permeabilization Buffer	Thermo Fisher	Cat# 00-8333-56
eBioscience™ Flow Cytometry Staining Buffer	Thermo Fisher	Cat# 00-4222-26
Proteinase K	Zymo	Cat# D3001-2-5
Polybrene	MilliporeSigma	Cat# TR-1003
Puromycin	MedChemExpress	Cat# HY-B1743
Doxycycline	Thermo Fisher	Cat# J60579.22
Propidium iodide	MilliporeSigma	Cat# P4170
Paraformaldehyde, methanol-free, 16%	Electron Microscopy Sciences	Cat# 15710
Triton X-100 (DNAse, RNAse, protease free)	Acros Organics	Cat# 327371000
RIPA lysis buffer	Thermo Fisher	Cat# 89900
SRC-selective tyrosine kinase inhibitor PP1	MilliporeSigma	Cat# 5678091MG
Ammonium Bicarbonate	Thermo Fisher	Cat# 393210010
Urea	Thermo Fisher	Cat# 29700
TCEP	Thermo Fisher	Cat# PG82080
Chloro-acetamide	Thermo Fisher	Cat# A15238.30
Trypsin/EDTA	Gibco	Cat# 25300054
Hydroxylamine	Thermo Fisher	Cat# A15398.0I
Acetonitrile	Thermo Fisher	Cat# 047138.M1
Formic acid	Thermo Fisher	Cat# 28905
Sorafenib (Bay 43-9006)	MedChemExpress	Cat# HY-10201
Ulixertinib (BVD-523)	MedChemExpress	Cat# HY-15816
Zegocractin (CM-4620)	MedChemExpress	Cat# HY-101942
Critical commercial assays
EasySep™ Direct Human PBMC Isolation Kit	STEMCELL Technologies	Cat# 19654
Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle	10x genomics	Cat# PN-1000283
Chromium Fixed RNA Kit, Human Transcriptome	10x genomics	Cat# PN-1000476
Dead cell removal kit	Miltenyi Biotec	Cat# 130-090-101
Quick-DNA/RNA Miniprep Kit	Zymo	Cat# D7005T
Dead cell apoptosis kit, Alexa 488 w/Annexin V PI	Invitrogen	Cat# V13241
RNeasy® Mini Kit	Qiagen	Cat# 74104
High-Capacity cDNA Reverse Transcription Kit	Thermo Fisher	Cat# 4368814
Pierce™ bicinchoninic acid (BCA) assay kit	Thermo Fisher	Cat# 23225
Nextera Library Prep Kit	Illumina	Cat# FC-131-1096
MinElute PCR purification	Qiagen	Cat# 28004
KAPA Library Quantification Kit	KAPA Biosystems	Cat# KK4824
CUTANA™ CUT&RUN Library Prep Kit	EpiCypher	Cat# 14-1001
Qubit 1X dsDNA HS Assay Kit	Thermo Fisher	Cat# Q33230
Pierce™ bicinchoninic acid (BCA) assay kit	Thermo Fisher	Cat# 23225
Duolink® In Situ Red Starter Kit Mouse/Rabbit	MilliporeSigma	Cat# DUO92101
Oligonucleotides
qPCR primers for IL8, GAPDH, and RN18S1: see Table S2	This study	N/A
gRNA sequences for STIM1, ORAI1, SPTAN1, and RAF1 knockouts: see Table S3	This study	N/A
Plasmid
lentiCRISPRv2	Addgene	Cat# 73179-LV
Lenti-iCas9-neo	Addgene	Cat# 85400
Lenti-multi-Guide plasmid	Addgene	Cat# 85401
pCDH-CMV-Nluc	Addgene	Cat#73038
pLenti CMV GFP Puro	Addgene	Cat# 658-5
pcDNA3-Venus-173-N-ORAI1	Addgene	Cat# 87618
pcDNA3.1-STIM1-Venus-173-C	Addgene	Cat# 87619
pCDH-CMV-Nluc	Addgene	Cat# 73038
Biological samples
Human peripheral blood mononuclear cells (PBMCs)	This study	N/A
THP-1 cells	ATCC	TIB-202
Lenti-X 293T	Takara Bio	Cat# 632180
Human CD14+ primary monocytes	Bloodworks Bio	4570-61
Antibodies
IL8 antibody	Invitrogen	Cat# 12808842; RRID: AB_2784632
Anti-Ras antibody	Abcam	Cat# AB108602; RRID: AB_10891004
Raf1 antibody	Abcam	Cat# AB137435; RRID: AB_3674177
Goat anti-Mouse Alexa Fluor 488	Life Technologies	Cat# A32723; RRID: AB_2633275
Goat anti-Rabbit, Alexa Fluor 594	Life Technologies	Cat# A32740; RRID: AB_2762824
Anti-Spectrin alpha chain (nonerythroid) Antibody	MilliporeSigma	Cat# MAB1622; RRID: AB_94295
p-ERK1/2 antibody	Cell Signaling	Cat# 9101; RRID: AB_331646
ERK1/2 antibody	Cell Signaling	Cat# 4695; RRID: AB_390779
IRDye 680RD Goat anti-Mouse	Li-Cor	Cat# 926-68070; RRID: AB_10956588
IRDye 800CW Goat anti-Rabbit	Li-Cor	Cat# 926-32211; RRID: AB_621843
c-Jun Antibody	Thermo Fisher	Cat# MA5-15881; RRID: AB_11153582
β-actin	Santa Cruz	Cat# SC-47778; RRID: AB_626632
Deposited data
snRNA-seq/snATAC-seq, ATAC, bulk-RNA, CUT&RUN data	This paper	NCBI GEO: GSE262146
CRISPRi screen data: See Data S1	This paper	NCBI GEO: GSE213184
scRNA-seq data of drug treatment	This paper	NCBI GEO: GSE291575
Phospho-proteomics: See Data S2	This paper	N/A
Software and algorithms
R 4.2.3	CRAN project	https://www.r-project.org/
Seurat	R package	Version: 4.3.0.1
Cellranger	10x Genomics	Version: 7.1.0
Cellranger-arc	10x Genomics	Version: 2.0.2
ArchR	R package	Version: 1.0.2
Signac	R package	Version: 1.10.9001
ArchRtoSignac	R package	Version: 1.0.5
dittoSeq	R package	Version: 1.18.0
trim_galore	Perl wrapper	Version: 0.6.10
STAR	Github	Version: 2.7.11b
FeatureCounts	Github	Version: v2.0.6
DESeq2	Github	Version: 1.38.3
MAGeCKFlute	R package	Version: 1.99.0
Bowtie2	Github	Version: 2.5.2
Picard	Github	Version: 2.27.5
macs2	Github	Version: 2.2.9.1
Bedtools	Github	Version: v2.31.0
Bedops	Github	Version: 2.4.41
edgeR	Github	Version: 3.40.2
ChIPseeker	Github	Version: 1.34.1
Scanpy	Python toolkit	Version: 1.11.2
FlowJo	FlowJo	Version: 10.8

Highlights.

• The supraphysiologic shear stresses of CPB activate inflammatory programs in PBMCs

• Shear stress increases AP-1 transcription factor binding to chromatin

• Shear stress activates SPTAN1/RAF1/SOCE signaling in non-adherent monocytic (THP-1) cells

• Targeting the SPTAN1/RAF1/SOCE pathway reduces shear-induced IL-8 expression in monocytes