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http://dx.doi.org/10.1038/s41586-019-1004-y | https://medicalxpress.com/news/2019-03-cancer-frequently-liver.html | Cancer most frequently spreads to the liver; here's why | When cancer spreads to another organ, it most commonly moves to the liver, and now researchers at the Abramson Cancer Center of the University of Pennsylvania say they know why. A new study, published today in Nature, shows hepatocytes—the chief functional cells of the liver—are at the center of a chain reaction that makes it particularly susceptible to cancer cells. These hepatocytes respond to inflammation by activating a protein called STAT3, which in turn increases their production of other proteins called SAA, which then remodel the liver and create the "soil" needed for cancer cells to "seed." The researchers show that stopping this process by using antibodies that block IL-6—the inflammatory signal that drives this chain reaction—can limit the potential of cancer to spread to the liver. "The seed-and-soil hypothesis is well-recognized, but our research now shows that hepatocytes are the major orchestrators of this process," said senior author Gregory L. Beatty, MD, Ph.D., an assistant professor of Hematology-Oncology at Penn's Perelman School of Medicine. Jae W. Lee, an MD/Ph.D. candidate in Beatty's laboratory, is the lead author. For this study, the team first used mouse models of pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer and currently the third leading cause of cancer death in the United States. They found that nearly all hepatocytes showed STAT3 activation in mice with cancer, compared to less than two percent of hepatocytes in mice without tumors. They then partnered with investigators at the Mayo Clinic Arizona and other Penn colleagues to show that this same biology could be seen in patients with pancreatic cancer as well colon and lung cancer. Genetically deleting STAT3 only in hepatocytes effectively blocked the increased susceptibility of the liver to cancer seeding in mice. The team collaborated further with investigators at the University of Kentucky to show that IL-6 controls STAT3 signaling in these cells and instructs hepatocytes to make SAA, which acts as an alarm to attract inflammatory cells and initiate a fibrotic reaction that together establish the "soil." "The liver is an important sensor in the body," Lee said. "We show that hepatocytes sense inflammation and respond in a structured way that cancer uses to help it spread." The study also found that IL-6 drives changes in the liver whether there's a tumor present or not, implying that any condition associated with increased IL-6 levels—such as obesity or cardiovascular disease, among others—could affect the liver's receptiveness to cancer. Researchers say this provides evidence that therapies which target hepatocytes may be able to prevent cancer from spreading to the liver, a major cause of cancer mortality. | Hepatocytes direct the formation of a pro-metastatic niche in the liver, Nature (2019). DOI: 10.1038/s41586-019-1004-y , www.nature.com/articles/s41586-019-1004-y Journal information: Nature | 10.1038/s41586-019-1004-y | Abstract The liver is the most common site of metastatic disease 1 . Although this metastatic tropism may reflect the mechanical trapping of circulating tumour cells, liver metastasis is also dependent, at least in part, on the formation of a ‘pro-metastatic’ niche that supports the spread of tumour cells to the liver 2 , 3 . The mechanisms that direct the formation of this niche are poorly understood. Here we show that hepatocytes coordinate myeloid cell accumulation and fibrosis within the liver and, in doing so, increase the susceptibility of the liver to metastatic seeding and outgrowth. During early pancreatic tumorigenesis in mice, hepatocytes show activation of signal transducer and activator of transcription 3 (STAT3) signalling and increased production of serum amyloid A1 and A2 (referred to collectively as SAA). Overexpression of SAA by hepatocytes also occurs in patients with pancreatic and colorectal cancers that have metastasized to the liver, and many patients with locally advanced and metastatic disease show increases in circulating SAA. Activation of STAT3 in hepatocytes and the subsequent production of SAA depend on the release of interleukin 6 (IL-6) into the circulation by non-malignant cells. Genetic ablation or blockade of components of IL-6–STAT3–SAA signalling prevents the establishment of a pro-metastatic niche and inhibits liver metastasis. Our data identify an intercellular network underpinned by hepatocytes that forms the basis of a pro-metastatic niche in the liver, and identify new therapeutic targets. Main To understand the mechanisms that underlie the formation of a pro-metastatic niche in the liver, we used the LSL-Kras G12D /+ ;LSL-Trp53 R127H /+ ;Pdx1-cre (KPC) mouse model of pancreatic ductal adenocarcinoma (PDAC) 4 , 5 . We looked for features of a pro-metastatic niche in the livers of over-16-week-old tumour-bearing KPC mice and 8- to 10-week-old non-tumour-bearing (NTB) KPC control mice, which lack PDAC but harbour pancreatic intraepithelial neoplasia (PanIN) 6 . Compared to control mice, the livers of KPC mice contained increased numbers of myeloid cells, accompanied by an increase in the deposition and expression of fibronectin and type I collagen (COL1) (Fig. 1a , Extended Data Fig. 1a–d ). Orthotopic implantation of KPC-derived PDAC cells into wild-type mice recapitulated these changes (Extended Data Fig. 1e–i ). As shown previously 7 , 8 , matrix deposition did not require myeloid cells (Extended Data Fig. 1j–l ). These results are consistent with evidence that myeloid cell accumulation and extracellular matrix deposition are key components of a pro-metastatic niche 7 , 8 , 9 , 10 . Fig. 1: Primary PDAC development induces a pro-metastatic niche in the liver. a , Images and quantification of myeloid cells, fibronectin (FN), and COL1 in the liver. Arrows indicate Ly6G + cells. Numbers in parentheses on plots indicate the number ( n ) of mice. Data pooled from two experiments. TB, tumour-bearing; NTB, non-tumour-bearing. b , Images of the liver and quantification of PDAC–YFP cells. Control mice ( n = 14) and NTB KPC mice ( n = 10) were intrasplenically injected with PDAC–YFP cells, and the liver was analysed after 10 days. Data representative of two independent experiments. c , Scatter plot of transcriptome data. FPKM, fragments per kilobase of exon per million mapped fragments ( n = 5 for both groups). Scale bars, 50 μm ( a ) and 1 cm ( b ). Statistical significance calculated using one-way analysis of variance (ANOVA) with Dunnett’s test ( a ) and two-tailed Mann–Whitney test ( b ). Data represented as mean ± s.d. Source data Full size image We next evaluated the susceptibility of the liver to metastatic colonization. Yellow fluorescent protein (YFP)-labelled KPC-derived PDAC cells (PDAC–YFP) 6 were injected into control mice and KPC mice. The metastatic burden was threefold higher in KPC mice, and metastatic lesions were detected in the livers of KPC mice at increased frequency and size with enhanced proliferation (shown using Ki-67) (Fig. 1b , Extended Data Fig. 2a, b ). Similar findings were observed using a YFP-negative KPC-derived cell line (Extended Data Fig. 2c, d ). Orthotopic implantation of PDAC cells also increased the susceptibility of the liver to metastatic colonization, and this finding was independent of the presence of CD4 + and CD8 + T cells (Extended Data Fig. 2e–s ). We next performed mRNA sequencing on RNA isolated from the livers of control and KPC mice. We identified 275 differentially expressed genes (Extended Data Fig. 3a, b , Supplementary Data 1 ) and found that genes upregulated in KPC mice were associated with immune-related processes (Extended Data Fig. 3c ). Notably, genes encoding myeloid chemoattractants, including SAA and members of the S100 family, were upregulated in KPC mice 11 , 12 , 13 (Fig. 1c , Extended Data Fig. 3d, e ). We also found enrichment of immune-related pathways, particularly the IL-6–JAK–STAT3 signalling pathway (Extended Data Fig. 3f , Supplementary Table 1 ). We validated our results by examining the livers of KPC mice for the presence of phosphorylated STAT3 (pSTAT3). STAT3 was activated in 80–90% of hepatocytes from KPC mice, compared to less than 2% of hepatocytes in control mice (Extended Data Fig. 3g, h ). By contrast, we did not detect activation of STAT1 signalling (Extended Data Fig. 3i ). Orthotopic implantation of PDAC cells also induced phosphorylation of STAT3 in hepatocytes (Extended Data Fig. 3j, k ). As IL-6 is fundamental to STAT3 signalling in hepatocytes 14 , we examined the livers of control mice ( Il6 +/+ ) and IL-6 knockout mice ( Il6 −/− ) orthotopically injected with PBS or PDAC cells. Tumour-implanted Il6 −/− mice displayed a decrease in STAT3 activation, particularly in hepatocytes (Fig. 2a , Extended Data Fig. 4a ). This loss in STAT3 activation was accompanied by reductions in myeloid cell accumulation and extracellular matrix deposition without alterations in the morphology and density of liver sinusoids (Fig. 2a and Extended Data Fig. 4a-d ). We also observed reduced expression of SAA, other chemoattractants, and extracellular matrix proteins (Fig. 2b , Extended Data Fig. 4e ). Genetic ablation of Il6 , however, did not alter proliferation, vascular density, or primary tumour growth (Extended Data Fig. 4f, g ). Il6 −/− mice were also less susceptible than control mice to metastatic colonization, and blockade of the IL-6 receptor (IL-6R) similarly inhibited the formation of a pro-metastatic niche in the liver (Fig. 2c–e , Extended Data Fig. 4h–s ). Notably, genetic ablation of Il6 or blockade of IL-6R did not completely inhibit STAT3 signalling, suggesting that IL-6-independent mechanisms may contribute to STAT3 activation. Fig. 2: IL-6 is necessary for the establishment of a pro-metastatic niche in the liver. a , b , n = 5 and 6 for Il6 +/+ mice and n = 4 and 5 for Il6 −/− mice orthotopically injected with PBS or PDAC cells, respectively. a , Quantification of pSTAT3 + cells, myeloid cells, and fibronectin. b , mRNA levels of Saa1 and Saa2 in the liver. c – e , n = 4 and 5 for Il6 +/+ mice and n = 4 for Il6 −/− mice orthotopically injected with PBS or PDAC cells, respectively. All groups were intraportally injected with PDAC–YFP cells on day 10. c , d , Images of liver and flow cytometric analysis. e , Quantification of PDAC–YFP cells. Data representative of two independent experiments ( a – e ). Scale bars, 1 cm. Statistical significance calculated using one-way ANOVA with Dunnett’s test. Data represented as mean ± s.d. Source data Full size image IL-6 promotes the development and progression of PDAC 15 , 16 , 17 , 18 . To identify the source of IL-6, we orthotopically injected PBS or PDAC cells into Il6 +/+ and Il6 −/− mice and measured the concentration of IL-6 at distinct anatomic sites (Extended Data Fig. 5a ). We detected IL-6 only in tumour-implanted Il6 +/+ mice, with the highest concentration of IL-6 found in the primary tumour (Extended Data Fig. 5b, c ). Although Il6 mRNA was undetectable in the liver, lung, and malignant cells, we observed Il6 mRNA in host cells adjacent to CK19-expressing PDAC cells (Extended Data Fig. 5d–g ). Human primary tumours displayed a similar expression pattern (Extended Data Fig. 5h ). Moreover, Il6 mRNA was detected in α-SMA + stromal cells located adjacent to PanIN and PDAC cells in KPC mice (Extended Data Fig. 5i–k ). We also found that primary pancreatic tumour supernatant activated STAT3 signalling in hepatocytes, and this was reduced in the presence of anti-IL-6R antibodies (Extended Data Fig. 6a, b ). These results show that IL-6 released by non-malignant cells within the primary tumour is a key mediator of STAT3 signalling in hepatocytes. To study a role for hepatocytes in directing liver metastasis, we generated mice that lacked Stat3 in hepatocytes ( Stat3 flox/flox Alb-cre ). Compared to control mice ( Stat3 flox/flox ), tumour-implanted Stat3 flox/flox Alb-cre mice lacked features of a pro-metastatic niche (Fig. 3a–c , Extended Data Fig. 6c ) and failed to produce SAA (Fig. 3d–f ). However, deletion of Stat3 in hepatocytes did not affect liver sinusoid density or morphology and did not alter the size, proliferation, or vascular density of the primary tumour (Extended Data Fig. 6d–f ). The livers of tumour-implanted Stat3 flox/flox Alb-cre mice were also less susceptible to metastatic colonization (Extended Data Fig. 6g–l ). In addition to its expression in hepatocytes (Extended Data Fig. 6m ), mRNA for SAA was detected in colonic cells 19 and in cells present in the periphery of the primary tumour (Extended Data Fig. 6n ). However, both cell types maintained comparable levels of SAA mRNA despite deletion of Stat3 in hepatocytes. Fig. 3: STAT3 signalling in hepatocytes orchestrates the formation of a pro-metastatic niche in the liver. a , Study design for b – f ( n = 4 for Stat3 flox/flox mice injected with PBS or PDAC cells; n = 8 and 7 for Stat3 flox/flox Alb-cre mice injected with PBS and PDAC cells, respectively). b , c , Quantification of pSTAT3 + cells, myeloid cells, and fibronectin. d , mRNA levels of Saa1 and Saa2 in the liver. e , Images of Saa1 and Saa2 mRNA in liver cells. Dashed lines and asterisks indicate sinusoids and hepatocytes, respectively. f , Concentration of circulating SAA. Data representative of two independent experiments ( a – f ). Scale bars, 50 μm. Statistical significance calculated using one-way ANOVA with Dunnett’s test. Data represented as mean ± s.d. Source data Full size image SAA proteins are acute phase reactants 20 . Consistent with elevated levels of circulating SAA in tumour-implanted mice (Fig. 3f ), patients with PDAC displayed elevated levels of circulating SAA (Extended Data Fig. 7a ). Overexpression of SAA and pSTAT3 by hepatocytes was also observed in five of seven patients with liver metastases (Fig. 4a , Extended Data Fig. 7b ). Notably, high levels of circulating SAA correlated with worse outcomes (Extended Data Fig. 7c ). Elevated levels of circulating SAA were also observed in patients with non-small-cell lung carcinoma (NSCLC) with liver metastases, and overexpression of SAA by hepatocytes was detected in the livers of patients with colorectal carcinoma (CRC) (Extended Data Fig. 7d, e ). In addition, compared to tumour-implanted control mice ( Saa +/+ ), double-knockout Saa1 −/− Saa2 −/− mice (hereafter referred to as Saa −/− mice) implanted with PDAC or MC-38 CRC cells failed to show features of a pro-metastatic niche in the liver, though genetic ablation of Saa1 and Saa2 had no effect on primary tumour growth (Fig. 4b–e , Extended Data Fig. 7f–s ). SAA was also necessary for IL-6-mediated formation of a pro-metastatic niche and for fibrosis and myeloid cell recruitment in the setting of liver injury (Extended Data Fig. 8 ). Fig. 4: SAA is a critical determinant of liver metastasis. a , Images of SAA in the livers of healthy donors (top) and patients with PDAC with liver metastases (bottom). Dashed lines and asterisks indicate sinusoids and hepatocytes, respectively. Data representative of one experiment. b , Quantification of pSTAT3 + cells, myeloid cells, and fibronectin ( n = 5 for all groups orthotopically injected with PBS or PDAC cells). For c – e , n = 4 and 5 for Saa +/+ mice and n = 5 and 6 for Saa −/− mice orthotopically injected with PBS and PDAC cells, respectively. All groups were intraportally injected with PDAC–YFP cells on day 10. c , d , Images of liver and flow cytometric analysis. e , Quantification of PDAC–YFP cells. Data representative of two independent experiments ( b – e ). Scale bars, 50 μm ( a ) and 1 cm ( c ). Statistical significance calculated using one-way ANOVA with Dunnett’s test. Data represented as mean ± s.d. Source data Full size image Tissue inhibitor of metalloproteinases 1 (TIMP1) 7 , 8 and macrophage migration inhibitory factor (MIF) 9 , 10 have been implicated in the promotion of metastasis. However, expression of these molecules was not affected by IL-6–STAT3–SAA signalling (Extended Data Fig. 9 ). We next determined whether formation of a pro-metastatic niche in the liver is dependent on the anatomical proximity of the pancreas to the liver. To this end, we looked for features of a pro-metastatic niche in the livers of CD45.1 and CD45.2 mice that were parabiotically joined (Extended Data Fig. 10a ). Although only CD45.2 mice were implanted with PDAC cells, both mice displayed myeloid cell accumulation and fibrosis in the liver (Extended Data Fig. 10b–g ), suggesting that formation of this niche is not dependent on the anatomical distance between the tumour and the liver. We also investigated whether SAA has a role in establishing a pro-metastatic niche in the lung. Development of PDAC in KPC mice induced accumulation of Ly6G + myeloid cells and deposition of fibronectin within the lung, but IL-6–STAT3–SAA signalling was not required for the formation of a pro-metastatic niche in the lung (Extended Data Fig. 10h–o ). Our data provide insight into the mechanisms that direct liver metastasis. Although recent studies have suggested a role for tumour-intrinsic factors in driving metastatic spread of cancer 7 , 8 , 9 , 10 , 21 , 22 , 23 , we provide evidence that inflammatory responses mounted by hepatocytes are critical to liver metastasis. Mechanistically, hepatocytes orchestrate this process through activation of IL-6–STAT3 signalling and the subsequent production of SAA, which alters the immune and fibrotic microenvironment of the liver to establish a pro-metastatic niche (Extended Data Fig. 10p ). Our findings suggest that therapies that target hepatocytes might prevent liver metastasis in patients with cancer. Methods Mice CD45.2 (wild type, C57BL/6J), CD45.1 (B6.SJL- Ptprc a Pepc b /BoyJ), Il6 knockout ( Il6 −/− , B6.129S2- Il6 tm1Kopf /J ) , Stat3 flox / flox (B6.129S1- Stat3 tm1Xyfu /J), and Alb - cre +/+ (B6.Cg-Tg(Alb-cre)21Mgn/J) mice were obtained from the Jackson Laboratory. Stat3 flox/flox mice were bred to Alb - cre +/+ mice to generate Stat3 flox /+ Alb-cre +/− mice, which were backcrossed onto Stat3 flox/flox mice to generate Stat3 flox/flox Alb-cre +/− mice. These mice were then bred to each other to create Stat3 flox/flox Alb-cre +/+ and Stat3 flox/flox Albumin-cre +/− mice ( Stat3 flox/flox Alb-cre ), and Stat3 flox/flox Albumin-cre −/− mice ( Stat3 flox/flox ). Kras LSL-G12D /+ Trp53 LSL-R172H /+ Pdx1-cre (KPC) mice and Trp53 LSL-R172H /+ Pdx1-cre (PC) mice were as previously described 4 , 5 . Saa1 and Saa2 double-knockout ( Saa −/− ) mice were as previously described 24 and provided by the University of Kentucky College of Medicine. Saa −/− mice used for experiments had been bred to obtain a 99.9% C57BL/6 background using the Jackson Laboratory Speed Congenic Service 24 . All transgenic mice were bred and maintained in the animal facility of the University of Pennsylvania. Animal protocols were reviewed and approved by the Institute of Animal Care and Use Committee of the University of Pennsylvania. In general, mice were monitored three times per week for general health and euthanized early based on defined endpoint criteria including tumour diameter ≥1 cm, ascites, lethargy, loss of ≥10% body weight, or other signs of sickness or distress. Clinical samples All patient samples were obtained after written informed consent and were de-identified. Studies were conducted in accordance with the 1996 Declaration of Helsinki and approved by institutional review boards of the University of Pennsylvania and the Mayo Clinic. To obtain plasma from healthy donors, patients with PDAC patients, and patients with NSCLC, peripheral whole blood was drawn in EDTA tubes (Fisher Scientific). Within 3 h of collection, blood samples were centrifuged at 1,600 g at room temperature for 10 min with the brake off. Next, the plasma was transferred to a 15-ml conical tube without disturbing the cellular layer and centrifuged at 3,000 g at room temperature for 10 min with the brake off. This step was repeated with a fresh 15-ml conical tube. The plasma was then stored at –80 °C until analysis. Biopsy results, computed tomography, and/or magnetic resonance imaging records were used to determine sites of metastasis in patients with PDAC or NSCLC whose plasma samples were used for assessment of SAA levels. Liver specimens from healthy donors were obtained by percutaneous liver biopsy, and acquisition of liver specimens from patients with liver metastases was as previously described 25 . Liver specimens from patients with CRC with liver metastases were obtained from the Cooperative Human Tissue Network (CHTN). Patient characteristics are shown in Supplementary Table 2 . Cell lines PDA.69 cell line (PDAC cells) was used for intrasplenic and orthotopic injection, and PDA.8572 cell line (PDAC–YFP cells) was used for intrasplenic, intraportal, and retro-orbital injections. These cell lines were derived from PDAC tumours that arose spontaneously in KPC mice, as previously described 4 , 26 . The MC-38 cell line, which was used for orthotopic implantation, was purchased from Kerafast. Cell lines were cultured in DMEM (Corning) supplemented with 10% fetal bovine serum (FBS, VWR), 83 μg/ml gentamicin (Thermo Fisher), and 1% GlutaMAX (Thermo Fisher) at 37 °C, 5% CO 2 . Only cell lines that had been passaged fewer than 10 times were used for experiments, and trypan blue staining was used to ensure that cells with >95% viability were used for studies. Cell lines were tested routinely for Mycoplasma contamination at the Cell Center Services Facility at the University of Pennsylvania. All cell lines used in our studies tested negative for Mycoplasma contamination. Animal experiments For all animal studies, mice of similar age and gender were block randomized in an unblinded fashion. Male and female mice aged between 8 to 12 weeks were used unless indicated otherwise. Mice were age- and gender-matched with appropriate control mice for analysis. Sample sizes were estimated based on pilot experiments and were selected to provide sufficient numbers of mice in each group for statistical analysis. For orthotopic and intrasplenic injections of pancreatic tumour cells, mice were anaesthetized using continuous isoflurane, and their abdomen was sterilized. After administering analgesic agents and assessing the depth of anaesthesia, we performed a laparotomy (5–10 mm) over the left upper quadrant of the abdomen to expose the peritoneal cavity. For orthotopic injection, the pancreas was exteriorized onto a sterile field, and sterile PBS or pancreatic tumour cells (5 × 10 5 cells suspended in 50 μl of sterile PBS) were injected into the tail of the pancreas via a 30-gauge needle (Covidien). Successful injection was confirmed by the formation of a liquid bleb at the site of injection with minimal fluid leakage. The pancreas was then gently placed back into the peritoneal cavity. For intrasplenic injection, 150 μl sterile PBS was drawn into a syringe and then sterile PBS or pancreatic tumour cells (5 × 10 5 cells suspended in 100 μl sterile PBS) was gently drawn into the same syringe in an upright position as previously described 27 . After the spleen was exteriorized onto a sterile field, pancreatic tumour cells were injected into the spleen via a 30-gauge needle. Successful injection was confirmed by whitening of the spleen and splenic blood vessels with minimal leakage of content into the peritoneum. Splenectomy was then performed by ligating splenic vessels with clips (Horizon) then cauterizing them to ensure that there was no haemorrhage. Afterwards, the remaining blood vessels were placed back into the peritoneal cavity. For both procedures, the peritoneum was closed with a 5-0 PDS II violet suture (Ethicon), and the skin was closed using the AutoClip system (Braintree Scientific). Following surgery, mice were given buprenorphine subcutaneously at a dose of 0.05-0.1 mg/kg every 4–6 h for 12 h and then every 6–8 h for 3 additional days. Mice that were orthotopically injected with pancreatic tumour cells were analysed after 20 days, unless indicated otherwise in study designs. Mice that were intrasplenically injected with PDAC cells were analysed after 10 days. For intraportal injection of pancreatic tumour cells and hydrodynamic injection of expression vectors, mice were anaesthetized using continuous isoflurane, and their abdomen was sterilized. After administration of analgesic agents, median laparotomy (10 mm) was performed, and the incision site was held open using an Agricola retractor (Roboz). After exposure of the peritoneal cavity, the intestines were located and exteriorized onto a sterile field surrounding the incision site to visualize the portal vein. Throughout the procedure, the intestines were kept hydrated with sterile PBS that was pre-warmed to 37 °C. For intraportal injection, sterile PBS or pancreatic tumour cells (5 × 10 5 cells suspended in 100 μl sterile PBS) were injected into the portal vein via a 30-gauge needle. Successful injection was confirmed by partial blanching of the liver. For hydrodynamic injection, 1 μg of pLIVE expression vectors was suspended in sterile saline corresponding to 8% of mouse body weight as previously described 28 . Vectors were injected into the portal vein via a 27-gauge needle within 5–8 s. Successful injection was confirmed by complete blanching and swelling of the liver. For both procedures, a sterile gauge was then held over the injection site for 1 min to ensure that no injected contents would leak into the peritoneal cavity. Afterwards, the intestines were placed back into the peritoneal cavity, and the peritoneum and skin were closed with a suture and autoclips, respectively. Following surgery, mice were given buprenorphine subcutaneously as described above. Intraportal injection of pancreatic tumour cells was performed on day 10, and metastatic burden in the liver was evaluated on day 20, unless indicated otherwise in study designs. For orthotopic implantation of colorectal tumour cells, wild-type mice were first subcutaneously injected with MC-38 (1 × 10 6 cells suspended in 100 μl of sterile PBS) into the right flank. After 10 days, mice were euthanized, and subcutaneous tumours were collected. Tumours were then cut into small pieces, each 3 × 3 mm in size, and placed in sterile PBS on ice until implantation. Mice were anaesthetized using isoflurane, and their abdomen was sterilized. Following administration of analgesic agents, median laparotomy was performed as described above. Implantation of colorectal tumour tissues into the caecum was then performed as previously described 29 . After we placed the intestines back into the peritoneal cavity, the peritoneum and skin were closed with a suture, and mice were given buprenorphine as described above. Mice were analysed after 10 days. For parabiotic joining of mice, female CD45.2 mice were orthotopically injected with sterile PBS or pancreatic tumour cells as described above and co-housed with age-matched female B6 CD45.1 mice. Each parabiotic pair was housed in a separate cage to maximize bonding between partners. After one week, parabiotic partners were anaesthetized using continuous isoflurane, and their flanks were sterilized. After administration of analgesic agents, longitudinal skin flaps from the lower limb to the upper limb were created, and everted skin flaps were sewn using a suture. In addition, the knees and olecranons of parabiotic partners were joined together using a suture for additional stabilization. Following surgery, mice were given buprenorphine subcutaneously at a dose of 0.05-0.1 mg/kg every 4-6 h for 5 days. Parabiotically joined mice were analysed after 20 days. For administration of antibodies, the abdomen of mice was sterilized, and anti-CD4 antibodies (GK1.5, 0.2 mg), anti-CD8 antibodies (2.43, 0.2 mg), anti-IL-6R antibodies (15A7, 0.2 mg), or rat isotype control antibodies (LTF-2, 0.2 mg) were suspended in 100 μl sterile PBS. Antibodies were subsequently injected into the peritoneum via a 30-gauge needle. All antibodies used in in vivo experiments were obtained from BioXCell. To deplete F4/80 + myeloid cells, clodronate-encapsulated liposomes (Liposoma) were administered by intraperitoneal injection according to the manufacturer’s protocol. For induction of liver injury, mice were intraperitoneally injected with CCl 4 (Sigma, 1 ml/kg body weight) dissolved in sunflower seed oil as previously described 30 . Detailed information on antibodies and reagents used in experiments can be in found in Supplementary Table 3 . Microscopic analysis For preparation of formalin-fixed paraffin-embedded (FFPE) sections, dissected tissues were fixed in 10% formalin for 24 h at room temperature, washed twice with PBS, and then stored in 70% ethanol solution at 4 °C until they were embedded in paraffin and sectioned at 5 μm. For preparation of cryosections, dissected tissues were embedded in Tissue-tek O.C.T. (Electron Microscopy Sciences) and frozen on dry ice. Frozen tissues were stored at –80 °C until they were sectioned at 7 μm. Automated immunohistochemistry, immunofluorescence, and RNA in situ hybridization were performed on FFPE sections using a Ventana Discovery Ultra automated slide staining system (Roche). Reagents were obtained from Roche and ACDBio (Supplementary Table 3 ) and used according to manufacturer’s protocol. Images were acquired using a BX43 upright microscope (Olympus), an Aperio CS2 scanner system (Leica), or an IX83 inverted multicolour fluorescent microscope (Olympus). Manual immunohistochemistry of mouse tissues for SAA was previously described 31 . For manual multicoloured immunofluorescence staining, O.C.T. liver cryosections were briefly air dried and fixed with 3% formaldehyde at room temperature for 15 min. For intracellular staining, sections were permeabilized with methanol at –20 °C for 10 min immediately after formaldehyde fixation. Sections were then blocked with 10% normal goat serum in PBS containing 0.1% TWEEN 20 for 30 min. For intracellular staining, 0.3% Triton X-100 was added to the blocking solution for permeabilization of cellular and nuclear membranes. Sections were incubated with primary antibodies (Supplementary Table 3 ) in the blocking solution for 1 h at room temperature or overnight at 4 °C, followed by washing with PBS containing 0.1% TWEEN 20. Sections were then incubated with secondary antibodies (Supplementary Table 3 ) in the blocking solution for 1 h at room temperature or overnight at 4 °C. After washing, sections were stained with DAPI to visualize nuclei and subsequently with Sudan Black B in 70% ethanol to reduce autofluorescence, as previously described 32 . Immunofluorescence imaging was performed on an IX83 inverted multicolour fluorescent microscope (Olympus). For quantification of cells and extracellular matrix proteins, five random fields were acquired from each biological sample. Flow cytometry Mice were euthanized, and the liver and lung were removed after the blood was drained by severing the portal vein and inferior vena cava. The liver and lung were rinsed thoroughly in PBS before mincing with micro-dissecting scissors into small pieces (<0.5 × 0.5 mm in size) at 4 °C in DMEM containing collagenase (1 mg/ml, Sigma-Aldrich), DNase (150 U/ml, Roche), and Dispase (1 U/ml, Worthington). Tissues were then incubated at 37 °C for 30 min with intermittent agitation, filtered through a 70-μm nylon strainer (Corning), and washed three times with DMEM. Cells were resuspended in ACK lysing buffer (Life Technologies) at room temperature for 15 min to remove red blood cells. After washing three times with DMEM, cells were counted and stained using Aqua dead cell stain kit (Life Technologies) following the manufacturer’s protocol. For characterization of immune cell subsets, cells were washed three times with PBS containing 0.2 mM EDTA with 2% FBS and stained with appropriate antibodies (Supplementary Table 3 ). For quantification of PDAC–YFP cells, cells were not stained with any antibodies. Lastly, cells were washed three times with PBS containing 0.2 mM EDTA with 2% FBS and examined using a FACS Canto II (BD Biosciences). Collection and analysis of the peripheral blood was as previously described 26 . FlowJo (FlowJo, LLC, version 10.2) was used to analyse flow cytometric data and generate 2D t -SNE plots. Detection of IL-6, SAA, and TIMP1 Mice that were orthotopically implanted with PDAC cells were euthanized, and primary tumours were removed and weighed. In addition, blood samples were collected from the portal vein and left ventricle of the heart using a 27-gauge needle. Tumours were rinsed thoroughly in PBS and minced with micro-dissecting scissors into small pieces (<0.5 × 0.5 mm in size) at 4 °C in serum-free DMEM at 1 mg of tissue per 1 μl medium. Tumour suspensions were then centrifuged at 12,470 g at 4 °C for 15 min, and tumour supernatant was collected and stored at –80 °C until analysis. A similar procedure was performed to obtain pancreas supernatant from mice that were orthotopically injected with PBS. To collect the serum, blood samples were allowed to clot at room temperature for 30 min. Samples were then centrifuged at 12,470 g at 4 °C for 15 min, and the serum was collected and stored at –80 °C until analysis. IL-6 levels in tumour or pancreas supernatant and serum were assessed using a cytometric bead array (BD Biosciences) following the manufacturer’s protocol. Samples were examined using a FACS Canto II (BD Biosciences), and data were analysed using FCAP Array (BD Biosciences, version 3.0). SAA and TIMP1 levels in mouse serum samples were measured using a commercially available enzyme-linked immunosorbent assay kit (Thermo Fisher) following the manufacturer’s protocol. Similarly, SAA levels in plasma samples collected from healthy donors and patients with PDAC as described under ‘Clinical samples’ were measured using a commercially available human enzyme-linked immunosorbent assay kit (Thermo Fisher) following the manufacturer’s protocol. RNA and quantitative PCR Mouse organs and cells were stored in TRIzol (Thermo Fisher) at –80 °C until analysis. Samples were thawed on ice and allowed to equilibrate to room temperature before RNA was isolated using a RNeasy Mini kit (Qiagen) following the manufacturer’s protocol. cDNA synthesis was performed as previously described 33 . Primers for quantitative PCR were designed using the Primer3 online program 34 , and sequences were analysed using the Nucleotide BLAST (NCBI) to minimize non-specific binding of primers. Primers were synthesized by Integrated DNA Technologies, and their sequences can be found in Supplementary Table 4 . Quantitative PCR was performed as previously described 33 . Gene expression was calculated relative to Actb (β-actin) using the ∆ C t formula, and fold change in gene expression was calculated relative to the average gene expression of control groups using the ∆∆ C t formula. Genes with C t greater than or equal to 30 were considered not detected. QuantSeq 3′ mRNA sequencing and data analysis RNA was isolated from the livers of control mice and NTB KPC mice as described above and submitted to the Genomics Facility at the Wistar Institute. After the quality of RNA was assessed using a 2100 Bioanalyzer (Agilent), samples were prepared using a QuantSeq 3′ mRNA-Seq library prep kit FWD for Illumina (Lexogen) following the manufacturer’s protocol and analysed on a NextSeq 500 sequencing system (Illumina). FASTQ files were uploaded to the BaseSpace Suite (Illumina) and aligned using its RNA-Seq Alignment application (version 1.0.0), in which STAR was selected to align sequences with maximum mismatches set to 14 as recommended by Lexogen. Output files were analysed using Cufflinks Assembly & DE application (version 2.1.0) in the BaseSpace Suite to determine differentially expressed genes, which were used to generate an expression heatmap and a FPKM scatter plot. In addition, these genes were analysed using ClueGO (version 2.3.3) 35 and CluePedia (version 1.3.3) 36 , which are applications of Cytoscape software (version 3.5.1) 37 . Functional grouping of biological processes was performed on the basis of kappa score. Gene Ontology data 38 , 39 downloaded on 23 January 2018 were used for analysis. Gene set enrichment analysis (version 3.0) 40 was used to determine biological processes that were differentially enriched in experimental groups. In vitro studies To isolate primary hepatocytes for in vitro studies, mice were anaesthetized using continuous isoflurane, and their abdomen was sterilized. After administering analgesic agents and assessing the depth of anaesthesia, we performed a laparotomy (10–15 mm) along the midline of the abdomen to expose the peritoneal cavity. The intestines were then located and exteriorized to visualize the inferior vena cava and portal vein. The inferior vena cava was cannulated via a 24 gauge Insyte Autoguard cathether (BD), and the liver was perfused using 50 ml liver perfusion medium (Thermo Fisher) at a flow rate of 8–9 ml/min using a peristaltic pump. At the start of perfusion, the portal vein was severed to drain the blood from the liver. Successful perfusion was confirmed by blanching of the liver, which was subsequently perfused using 50 ml liver digest medium (Thermo Fisher) at the same flow rate. Both liver perfusion medium and liver digest medium were pre-warmed to 42 °C in a water bath. After perfusion, the liver was carefully transferred to a Petri dish containing William’s E medium (Sigma) supplemented with 10% FBS, 83 μg/ml gentamicin, and 1% GlutaMAX. To dissociate hepatocytes from the liver, cell scrapers were used to create small cuts (5 mm) on the surface of the liver, and the tissue was gently shaken. Dissociated cells were then filtered through a 100-μm nylon strainer (Corning) and centrifuged at 50 g at 4 °C for 5 min. After the supernatant was discarded, cells were resuspended in a solution consisting of isotonic Percoll (Sigma) and supplemented William’s E medium (2:3 ratio). Cells were then centrifuged at 50 g at 4 °C for 10 min to obtain a pellet enriched in hepatocytes. The supernatant was discarded, and hepatocytes were resuspended in supplemented William’s E medium. Cell viability and number were determined using trypan blue staining, and 5 × 10 4 hepatocytes were seeded in each well of a 48-well plate pre-coated with collagen. Hepatocytes were incubated in supplemented William’s E medium for 4 h at 37 °C, 5% CO 2 to allow attachment to the plate. The medium was then switched to HepatoZYME-SFM (Thermo Fisher) supplemented with 83 μg/ml gentamicin and 1% GlutaMAX. Medium was replenished every 24 h for the next 48–72 h. For hepatocyte activation assays, hepatocytes were incubated in supplemented HepatoZYME-SFM mixed with (i) serum-free DMEM, (ii) primary pancreatic tumour supernatant, or (iii) serum-free DMEM containing 250 ng/ml IL-6 (Peprotech) for 30 min at 37 °C, 5% CO 2 . All mixtures were made in a 1:1 ratio, and each condition was run in triplicate. For the in vitro IL-6R blockade experiment, hepatocytes were pre-incubated with 5 μg/ml anti-IL-6R antibodies for 2 h before being stimulated with tumour supernatant. After stimulation, medium was carefully removed, and formaldehyde and methanol were used to fix and permeabilize hepatocytes, respectively, as described above. Hepatocytes were then stained for pSTAT3 (Supplementary Table 3 ), and their nuclei stained with DAPI. Immunofluorescence imaging was performed on an IX83 inverted multicolour fluorescent microscope (Olympus). Statistical analysis Statistical significance was calculated using Prism (GraphPad Software, version 7) unless indicated otherwise. Multiple comparisons testing was performed using one-way ANOVA with Dunnett’s test. Paired group comparisons test was carried out using two-tailed Wilcoxon matched-pairs signed rank test. Unpaired group comparisons test was performed using two-tailed unpaired Student’s t test or two-tailed Mann–Whitney test. Comparison of Kaplan–Meier overall survival curves was performed using log-rank (Mantel-Cox) test. P values less than 0.05 were treated as significant. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment, unless stated otherwise. Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this paper. Data availability QuantSeq 3′ mRNA sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE109480 . Source Data are provided for all figures and extended data figures. All data are available from the corresponding author upon reasonable request. | Medicine |
http://dx.doi.org/10.1038/s41592-019-0640-3 | https://phys.org/news/2019-11-scientists-implantable-magnet-resonance-detector.html | Scientists develop first implantable magnet resonance detector | A team of neuroscientists and electrical engineers from Germany and Switzerland developed a highly sensitive implant that enables to probe brain physiology with unparalleled spatial and temporal resolution. They introduce an ultra-fine needle with an integrated chip that is capable of detecting and transmitting nuclear magnetic resonance (NMR) data from nanoliter volumes of brain oxygen metabolism. The breakthrough design will allow entirely new applications in the life sciences. The group of researchers led by Klaus Scheffler from the Max Planck Institute for Biological Cybernetics and the University of Tübingen as well as by Jens Anders from the University of Stuttgart identified a technical bypass that bridges the electrophysical limits of contemporary brain scan methods. Their development of a capillary monolithic nuclear magnetic resonance (NMR) needle combines the versatility of brain imaging with the accuracy of a very localized and fast technique to analyze the specific neuronal activity of the brain. "The integrated design of a nuclear magnetic resonance detector on a single chip supremely reduces the typical electromagnetic interference of magnetic resonance signals. This enables neuroscientists to gather precise data from minuscule areas of the brain and to combine them with information from spatial and temporal data of the brain´s physiology," explains principal investigator Klaus Scheffler. "With this method, we can now better understand specific activity and functionalities in the brain." According to Scheffler and his group, their invention may unveil the possibility of discovering novel effects or typical fingerprints of neuronal activation, up to specific neuronal events in brain tissue. "Our design setup will allow scalable solutions, meaning the possibility of expanding the collection of data from more than from a single area—but on the same device. The scalability of our approach will allow us to extend our platform by additional sensing modalities such as electrophysiological and optogenetic measurements," adds the second principal investigator Jens Anders. The teams of Scheffler and Anders are very confident that their technical approach may help demerge the complex physiologic processes within the neural networks of the brain and that it may uncover additional benefits that can provide even deeper insights into the functionality of the brain. With their primary goal to develop new techniques that are able to specifically probe the structural and biochemical composition of living brain tissue, their latest innovation paves the way for future highly specific and quantitative mapping techniques of neuronal activity and bioenergetic processes in the brain cells. | Jonas Handwerker et al. A CMOS NMR needle for probing brain physiology with high spatial and temporal resolution, Nature Methods (2019). DOI: 10.1038/s41592-019-0640-3 Journal information: Nature Methods | 10.1038/s41592-019-0640-3 | Abstract Magnetic resonance imaging and spectroscopy are versatile methods for probing brain physiology, but their intrinsically low sensitivity limits the achievable spatial and temporal resolution. Here, we introduce a monolithically integrated NMR-on-a-chip needle that combines an ultra-sensitive 300 µm NMR coil with a complete NMR transceiver, enabling in vivo measurements of blood oxygenation and flow in nanoliter volumes at a sampling rate of 200 Hz. Main Methods based on nuclear magnetic resonance (NMR) are powerful analytical techniques in the life sciences, using nuclear spins as specific nanoscopic probes. Despite substantial advances in magnetic resonance (MR) hardware and methodology, NMR is still limited by its poor sensitivity (compared, for example, with optical methods), hindering in particular its use in the study of brain physiology and pathology. Recently, integrated circuit (IC)-based NMR systems have been introduced 1 , 2 , 3 , 4 , 5 to simplify the hardware complexity of MR experiments and to boost sensitivity. Integration of the MR detection coil with the transceiver on a single IC 4 , 5 laid the foundation for millimeter-size, sensitive MR systems for in situ and in vivo applications such as palm-size NMR spectrometry 1 and NMR spectroscopy of single cells 5 . Here, we present a monolithic needle-shaped NMR-on-a-chip transceiver (Fig. 1a,b ) that makes the advantages of IC-based NMR available for various applications in neuroscience. With its miniaturized on-chip coil, low-noise performance and compact, 450 µm-wide needle design, our NMR-on-a-chip transceiver simultaneously improves sensitivity as well as spatial and temporal resolution. In contrast to conventional microcoils 6 , 7 , the micrometer-scale interconnecting wires between the on-chip coil and the electronics combined with the fully differential design reduce the pickup of parasitic MR signals and electromagnetic interference. This enables interference-free in vivo experiments in a defined region of interest. Compared to conventional functional MR imaging (fMRI), the on-chip microcoil removes the need for time-consuming spatial encoding and allows for a continuous recording of MR signals in a nanoliter volume with millisecond resolution. Fig. 1: Schematic overview of the target application of the needle-shaped NMR-on-a-chip transceiver, the ASIC design and the experimental setup. a , The NMR needle is inserted into the target brain area, for example the somatosensory cortex, to perform localized and fast functional MR experiments. b , Fully integrated NMR-on-a-chip spectrometer with an on-chip planar broadband detection coil. The transceiver electronics include a low-noise receiver with quadrature demodulation, an H-bridge-based PA and a frequency synthesizer (containing a phase-frequency detector (PFD), a charge pump (CP) and a quadrature signal generator (IQ)). c , Experimental setup around the NMR needle: the ASIC is glued and bonded on a small carrier PCB and connected via a ribbon cable to the signal conditioning PCB. This setup can be mounted either on a carrier with a sample container and a conventional 8 mm surface coil as reference for system characterization, such as linewidth, sensitivity and SNR, and MR imaging (in vitro setup) or on an animal bed for neuronal experiments to measure changes in blood oxygenation and flow in rats (in vivo setup). The bed or carrier is placed inside a 14.1 T small-animal scanner and the system is completed by a commercial data-acquisition card and a LabVIEW-based console located in the control room. Full size image To achieve the required detection sensitivity in a form factor that is suitable for localized in vivo experiments in brain tissue, we realized a complete NMR spectrometer as a complementary metal-oxide-semiconductor (CMOS) application-specific integrated circuit (ASIC) (Fig. 1b ). This low-power (20 mW) NMR-on-a-chip transceiver features an on-chip, 24-turn, 300 µm outer diameter, transmit/receive (TX/RX) NMR coil. The RX path contains a complete quadrature receiver with an overall noise figure of 0.7 dB including a phase-locked loop (PLL)-based frequency synthesizer and protection switches for the low-noise amplifier (LNA). The TX path features an H-bridge power amplifier (PA) operating from a 3.3 V supply and driven by the on-chip PLL that produces a maximum coil current of 15 mA at 600 MHz. Owing to its amplitude and phase modulation capabilities, the on-chip electronics allow for the use of standard imaging sequences and spectroscopy techniques. In mechanical postprocessing, we first ground the manufactured chips down to a thickness of 100 µm and then shaped them as a needle with a wafer dicer. We used two different setups for in vitro characterization and for in vivo neuronal rat experiments in a 14.1 T small-animal scanner (Fig. 1c ). After first-order manual shimming, the NMR needle achieves a spectral linewidth of 12 Hz in a water phantom (Supplementary Fig. 1 ) and 53 Hz for in vivo experiments (Supplementary Fig. 2 ). We determined the sensitivity of the NMR needle using a three-dimensional gradient echo (3DGRE) sequence, resulting in a sensitive volume of 9.8 nl (Fig. 2a and Supplementary Fig. 3 ) and a time-domain spin sensitivity of \(2.0 \times 10^{13}{\,\mathrm{spins}}\,\mathrm{per}\,\sqrt {{\mathrm{Hz}}}\) . Compared to a conventional 8 mm surface coil, the NMR needle’s signal-to-noise ratio (SNR) per spin is 40 times higher ( Methods ). We obtained 3DGRE images of a polyimide phantom with an isotropic resolution of 13 µm in less than 15 min, demonstrating the excellent MR imaging capabilities of the NMR needle (Supplementary Fig. 4 ). Fig. 2: In vitro measurement of the sensitive volume and representative experimental results from in vivo rat forepaw stimulation experiments. a , Single-shot (that is, no averaging) 3DGRE image of the sensitive volume V sens of the NMR needle immersed in 10 mM Gd-doped water ( N = 1). b , Coronal anatomical MR image recorded with a conventional surface coil, showing the precise needle location (no averaging, N = 1). The inset shows an overlay from EPI fMRI with a contralateral activation from the stimulation of the left paw in the implantation region of the needle (average of N = 20 stimulation blocks). c , Axial anatomical MR image showing the precise needle location and implantation depth ( N = 1). The inset shows an overlay from EPI fMRI ( N = 20). The presented data for b and c are representative of 12 animals. d , Contralateral BOLD response showing activations in each of the 20 identical 30 s stimulation blocks of a T R = 5 ms acquisition sequence ( N = 1 block for each curve). The stimulation period t stim = 6 s in each block is indicated by the gray background. e , Mean μ and standard deviation σ of contralateral BOLD responses (average of N = 20 blocks) from EPI fMRI and NMR needle FIDs for stimulations of the left paw with different temporal (t) resolutions (for tSNR calculation see Methods and Supplementary Table 1 ). f , Ipsilateral BOLD responses ( N = 20) from EPI fMRI and NMR needle FIDs for stimulations of the right paw. g , Fit of Δ M 0, i from the functional measurements in e indicating the inflow effect for short T R ( N = 20). h , Fit of \({\mathrm{\Delta }}R_{2,{i}}^ \ast\) from the functional measurements in e for multiple T R ( N = 20). i , Combined plot of mean values μ from g and h ( N = 20). The presented data for d to i are representative of seven animals. Full size image As an in vivo benchmark application of our NMR sensing platform against conventional MR systems for neuronal measurements, we selected the detection of changes in blood flow and oxygenation in rats upon electrical forepaw stimulation. For this purpose, we slowly inserted the NMR needle 1.5 mm deep into the rat’s somatosensory cortex 8 ( Methods ). The in vivo setup (Fig. 1c ) allows for the recording of the typical NMR response after a pulse excitation, the so-called free induction decay (FID), with the NMR needle as well as conventional fMRI using echo planar imaging (EPI) with a surface coil. We also used the surface coil to determine the needle location via high-resolution anatomical MR imaging of the implantation region. The overlays of conventional EPI data on the anatomical MR images show the responses to the stimulation at the needle location (Fig. 2b,c ). The effect of hemodynamic changes on the time course of the FID acquired with the NMR needle is twofold. First, changes in cerebral blood flow (CBF) modulate the initial FID amplitude through a change of inflowing unsaturated blood into the sensitive volume of the coil. Furthermore, changes in local oxygenation of blood (BOLD effect) alter the decay rate \(R_{2}^{\ast}=1/T_{2}^{\ast}\) of the FID. To capture both effects, we calculated the area under each magnitude FID, obtaining a time series with a temporal resolution of up to 200 Hz. We corrected these time series further for temporal stability and physiological noise (Supplementary Fig. 5 ). A stimulation experiment to measure CBF and BOLD changes consisted of 20 identical 30 s blocks with a stimulation for t stim = 6 s, followed by a resting period of 24 s ( Methods ). A corrected time course for an NMR repetition time of T R = 5 ms shows a contralateral response to each of the 20 identical stimulations of the left paw (Fig. 2d ). The signal detected with the NMR needle upon stimulation has an amplitude around 1% and displays a very small delay with respect to the onset and the end of the 6 s stimulation. Signals measured with the needle at T R = 1 s have a similar lineshape, relative signal change Δ S / S and temporal SNR (tSNR) as the reference EPI time course (Fig. 2e ), while being recorded in a substantially lower sensitive volume (9.8 nl compared to a region of interest (ROI) of 12 µl). Compared to a single EPI voxel, the volume-normalized tSNR of the NMR needle at T R = 1 s is 150-fold increased (Supplementary Table 1 ). Increasing the sampling rate of the needle FIDs to 20 Hz and 200 Hz results in a faster tracking of hemodynamic changes. The ipsilateral responses to a stimulation of the right paw (Fig. 2f ) show no measurable effect in any of the measurements, which confirms that the signals measured in the contralateral cortex (Fig. 2e ) represent hemodynamic responses. To separate the changes of local CBF and blood oxygenation in the contralateral responses, we fitted each individual i th FID time course to a physiological model and verified the results by numerical simulations ( Methods ). For a repetition time T R = 1 s, the blood within the sensitive region fully exchanges within one T R , therefore no inflow-related magnitude change Δ M 0, i was observed (Fig. 2g ). The Δ M 0, i for both short T R (5 ms and 50 ms) are around 0.5%, which corresponds to a change in CBF of about 15–30 ml per 100 g per min (Supplementary Fig. 6a ), or 13% to 25% assuming a baseline CBF of 120 ml per 100 g per min(ref. 9 ). This is in the lower range of reported values of 20% to 90% based on MR perfusion measurements 10 , 11 , most likely due to different anesthetics or a potential inclusion of larger vessels. Observed changes in local blood oxygenation \({\mathrm{\Delta }}R_{2,{i}}^ \ast\) are between 1.5 Hz and 2 Hz across all chosen T R (Fig. 2h ), which relates to a local oxygenation change around 15% to 20% (Supplementary Fig. 6b ). The measured changes in \({\mathrm{\Delta }}R_{2,{i}}^ \ast\) are comparable to quantitative \({\mathrm{\Delta }}T_2^ \ast\) measurements in humans and rats ranging between 1 Hz and 6 Hz (refs. 12 , 13 ). Despite the unprecedented temporal resolution of 5 ms, our results indicate neither the presence of an initial dip (a short and small BOLD signal decrease attributed to oxygenation decrease prior to any subsequent blood flow and oxygenation increase 14 ) nor a mismatch between CBF and oxygenation changes (Fig. 2i ). Our NMR needle targets the deep cortical layers of rodents where no initial dip was detected in a previous study 15 . Conventional fMRI studies with large voxel sizes often report a substantial mismatch between CBF and oxygenation changes 16 . However, combined optical measurements of CBF and oxygenation show that this mismatch is only visible at the venous side, but not at the capillary level or at the artery side 17 . Our data thus support, in agreement with optical measurements 17 , that the temporal mismatch between oxygenation and CBF changes is strongly reduced in deep cortical regions. Although preliminary in nature, our results demonstrate the power of CMOS-based in vivo MR experiments and the NMR needle’s potential for future applications in neuroscience. Applications may reveal currently unknown dynamics in the laminar-specific hemodynamic response and the underlying physiology of fMRI with layer-specific resolution, and even effects that are not related to hemodynamics. The NMR needle allows correlation of the continuously detectable and locally acquired MR signals with other recordings, such as local field potentials or optically detected changes in local calcium concentration at a comparable sampling bandwidth and spatial resolution. This provides the possibility of discovering novel effects or fingerprints of neuronal activation inside the continuously evolving MR magnetization. This might include the detection of local geometric changes, for example cell swelling, or the direct detection of bulk neuronal currents through their induction of a local magnetic field 18 , 19 . The sensitive volume of the NMR needle is comparable to the thickness of a single cortical layer, the extension of a cortical column or small subcortical nuclei. Moreover, the scalable CMOS design is well suited to form an array of small coils along the shaft of the needle to collect signals from different cortical layers simultaneously with individual coils and without the need to move the sensor. Additionally, the NMR needle can be extended by an array of electrophysiological electrodes individually connected to integrated preamplifiers for (multisite) in vivo electrical stimulation and recording 20 , 21 on a single sensor chip, thus opening up the path for future multi-modal brain sensing platforms. As the presented NMR needle achieves a similar spatiotemporal resolution as electrophysiology or optical brain recording while offering the specificity and versatility of NMR, IC-based in vivo NMR is a promising approach to close the gap between these complementary imaging modalities. Therefore, we believe that our approach can help to disentangle physiologic processes within the neural network and that this technology can potentially uncover MR effects beyond the conventional hemodynamic signal responses to provide even deeper insights. Methods CMOS chip design and operation The needle-shaped NMR-on-a-chip transceiver has been fabricated in a 130 nm CMOS technology from GlobalFoundries. The postprocessed chips are 3,000 µm long, 450 µm wide and 100 µm thick. The microcoil is located at the needle tip, which is formed in a mechanical postprocessing step, while the bondpads are placed on the opposite end to allow for an implantation depth up to 2 mm. The transceiver electronics substantially improve and extend a previously published version of an NMR-on-a-chip transceiver 4 for the target application of this paper. Here, the extreme form factor required special care in the design of all electrical interconnects to avoid an undesirable coupling into the RX path from the TX path or the frequency synthesizer. The on-chip RX path is fully differential to suppress Hall and magnetoresistive effects inside the strong B 0 field and incorporates a current-reuse LNA with a common mode feedback. The LNA provides an input referred voltage noise of \(1.26\,{\mathrm{nV}}\,\mathrm{per}\,\sqrt {{\mathrm{Hz}}}\) over a bandwidth from 30 MHz to 700 MHz. At 600 MHz, the LNA degrades the intrinsic on-chip coil SNR by 9% corresponding to a noise figure of 0.7 dB. Importantly, in contrast to conventional MR coil arrays, the high-impedance on-chip LNA provides an efficient coil decoupling, allowing for an arbitrary placement of multiple coils along the needle for future microcoil arrays. Active quadrature Gilbert cell mixers follow the LNA and demodulate the detected NMR signal at f NMR to the desired low to intermediate frequency (low-IF) f IF in the range of 10 kHz to 100 kHz. The signals at the low-IF are further amplified and converted to single-ended signals v out,I and v out,Q in the buffer stage to minimize the number of required bondpads. The TX path operates at 3.3 V compared to the 1.5-V RX supply to increase the maximum coil current for pulsed excitation. The H-bridge PA is a nonresonant design to maximize the current in the NMR coil for a given supply voltage 22 and can be disabled during RX operation without requiring additional series switches, which would otherwise decrease the TX performance. The TX signal and the quadrature local oscillator signals f LO,sin and f LO,cos for the RX path are generated from a low-frequency reference using an integer- N PLL, enabling micrometer-length radio frequency interconnects and facilitating the electrical connection of the needle. A frequency shift keying (FSK) of the PLL reference f ref allows for an on-resonance excitation and a low-IF RX operation outside the 1/ f noise region of the receiver. In vitro setup Each postprocessed NMR needle was glued to a carrier printed circuit board (PCB), with two-thirds of the chip extending beyond the PCB edge to enable implantation. The ASIC was wire bonded onto the carrier PCB, which was in turn connected to the 6 × 3 cm 2 large signal conditioning PCB containing amplifiers for the NMR signals, clock and signal buffers and the power supply for the ASIC. The same assembly was used in the in vitro and the in vivo setups (Fig. 2b ), both being designed for operation inside a 14.1 T, 26 cm horizontal bore magnet (Magnex Scientific). The in vitro setup features a sample basin with a diameter of 13 mm and a height of 7 mm, which was filled with 700 µl of deionized or 10 mM gadolinium (Gd)-doped water. Conventional planar coils with 8 mm and 10 mm diameters were placed below the basin and interfaced to the standard BioSpec spectrometer (Bruker BioSpin). Simulation and measurement of the sensitive volume Finite-element electromagnetic simulations of the on-chip coil’s unitary B field, B u , were carried out to characterize its inhomogeneity (Supplementary Fig. 7 a–c), leading to a nonuniform flip angle distribution in the sample during TX and a nonuniform sensitivity during RX 23 . The flip angle was selected by choosing the output current of the H-bridge amplifier via TX supply modulation and an appropriate pulse length. The resulting signal intensity versus pulse length for the maximum TX supply of 3.3 V (Supplementary Fig. 7 d) has its peak value at 13 µs. The sensitive volume of the planar microcoil with its inhomogeneous field distribution was defined consistently throughout the study using the following definition. An image slice parallel to the coil surface at a distance of 0.1 d coil , where d coil is the diameter of the coil, is selected in which the signal ROI is defined as a centered square with a side length of 0.5 d coil . The image signal \(\hat S\) is determined from the mean μ of the individual voxel intensities I S ,1 ,…, I S , i within the signal ROI according to \(\hat S = \mu \left( {I_{S,1}, \ldots ,I_{S,i}} \right)\) . The sensitive volume is then defined as the volume with a signal amplitude of at least 10% of the signal \(\hat S\) , resulting in a simulated sensitive volume of the microcoil of 9.5 nl (Supplementary Fig. 3 a–d). The simulation results were validated experimentally in 10 mM Gd-doped water. The nutation curve was measured in simple pulse-acquire experiments (Supplementary Fig. 7 d). The sensitive volume was assessed in a 3DGRE imaging experiment using T R = 30 ms, pulse time T P = 10 μs, echo time T E = 4.77 ms, acquisition time T acq = 5.1 ms, number of averages N avg = 1, matrix size 128 × 128 × 128, isotropic voxel size 13 µm, field of view (FOV) 1.7 × 1.7 × 1.7 mm 3 , scan time 8 min 12 s and manual shim. The measured sensitive volume was 9.8 nl and simulation and measurement were in good agreement. B 0 map and manual shim The B 0 map in Supplementary Fig. 8 was recorded with the in vitro setup using a 10 mm surface coil underneath the deionized water-filled basin using two 3DGRE sequences with different echo times T E1 and T E2 with T R = 47.86 ms, flip angle FA = 30°, T E1 = 2.56 ms, T E2 = 5.88 ms, T acq = 2.15 ms, N avg = 1, matrix size 256 × 175 × 256, isotropic voxel size 45 µm, FOV 11.5 × 7.9 × 11.5 mm 3 , scan time 35 min 44 s and global shim. The NMR needle causes local susceptibility variations in the order of ±500 Hz. To improve the linewidth and thereby the needle’s frequency domain SNR, a manual shim procedure was used, where the three first-order shim gradients were iteratively changed until a minimum linewidth was found. Modifying higher-order shim gradients did not result in a significant improvement. The resulting x gradient was between −11,000 Hz cm −1 to −8,000 Hz cm −1 , while the y and z gradients were between −2,000 Hz cm −1 and +2,000 Hz cm −1 . The B 0 map in Supplementary Fig. 8b has a field gradient in the x direction of −10,000 Hz cm −1 , which is in good agreement with the results found during the manual shim procedure. A spectral linewidth of 12 Hz (corresponding to 0.02 ppm) was achieved on a water phantom (Supplementary Fig. 1b ). Under in vivo conditions, the intrinsic relaxation time \(T_2^ \ast\) of brain tissue is about 20 ms to 30 ms (corresponding to a linewidth of 16 Hz to 11 Hz) and can be even shorter in regions with high blood volume fraction. Thus, the needle linewidth of 12 Hz does not substantially degrade the intrinsic tissue linewidth for in vivo NMR experiments. Image SNR and system sensitivity The sensitivity of the described microcoil was compared with a conventional 8 mm-diameter surface coil. The time-domain spin sensitivity is defined as the minimum detectable number of spins with an SNR of three in 1 s of measurement time. This is a suitable figure of merit for comparing coil sensitivities because it directly relates to the minimum detectable voxel size. It can be computed from the image SNR per spin, which in turn can be determined from a single magnitude image according to the National Electrical Manufacturers Association Standards Publication MS 1-2008 (ref. 24 ) as detailed in Anders et al. 3 . For the 8- mm coil, 3DGRE imaging with T R = 30 ms, FA = 35°, T E = 4.77 ms, T acq = 1.28 ms, N avg = 1, matrix size 128 × 128 × 128, isotropic voxel size 100 µm, FOV 13 × 13 × 13 mm 3 and scan time 8 min 12 s was used (Supplementary Fig. 9b ). Using the definition of \(\hat S\) , a noise ROI (10 × 10 voxel in each of the four corners) to determine the image noise \(\hat \sigma _{\mathrm{N}}\) and following the procedure of Anders et al. 3 , the time-domain spin sensitivities for both coils were determined. With SNR needle = 68.9 and SNR coil = 383 from Supplementary Fig. 9 , voxel sizes \(\Delta_{{\mathrm{needle}}}^3 = \left( {13\,{{\upmu {\mathrm{m}}}}} \right)^3\) and \(\Delta_{{\mathrm{coil}}}^3 = \left( {100\,{{\upmu {\mathrm{m}}}}} \right)^3\) , T ACQ,needle = 5.1 ms, T ACQ,coil = 1.28 ms ( T R = 30 ms, T E = 4.77 ms, number of phase encoding steps N PE = 128 × 128, N avg = 1, spin density \(N_{{\mathrm{s}},{\mathrm{H}}_2{\mathrm{O}}} = 6.7 \times 10^{28}\,{\mathrm{spins}}\,{\mathrm{m}}^{-3}\) for both), the time-domain spin sensitivities of the two coils are \(8.0 \times 10^{14}\,{\mathrm{spins}}\,{\mathrm{per}}\,\sqrt {{\mathrm{Hz}}}\) for the 8 mm coil and \(2.0 \times 10^{13}{\mathrm{spins}}\,{\mathrm{per}}\,\sqrt {{\mathrm{Hz}}}\) for the NMR needle. This corresponds to a 40-fold improvement in spin sensitivity and a 1,600-fold improvement in imaging time of the presented NMR needle compared to the surface coil. MR imaging The imaging capabilities of the NMR needle were demonstrated using a polyimide foil phantom with 50 × 50 μm 2 laser-cut square openings (Supplementary Fig. 4 ). This phantom was immersed in 10 mM Gd-doped water and the needle placed in close proximity to the foil. A 3DGRE image with an isotropic resolution of 13 µm was recorded using T R = 50 ms, T P = 8 μs, T E = 4.77 ms, T acq = 5.1 ms, N avg = 1, matrix size 128 × 128 × 128, isotropic voxel size 13 µm, FOV 1.7 × 1.7 × 1.7 mm 3 , scan time 13 min 39 s and manual shim. Animal preparation Fifteen healthy, anesthetized rats (Sprague-Dawley, male, 402 ± 49 g, 11 ± 2 weeks) were examined in the 14.1-T small-animal scanner. The study was approved by the local authorities (Regierungspräsidium Tübingen, Germany) and was in full compliance with the guidelines of the European Community for the care and use of laboratory animals. The rats were anesthetized with urethane (1.2 g kg −1 body weight, more if necessary to maintain anesthesia). The body temperature was monitored via a rectal probe and kept constant at around 37 °C by a heating pad. Breathing rate, oxygen saturation and heart rate were monitored throughout surgery and experiment with a pulse oximeter (MouseOx, Starr Life Sciences). During surgery, the animal was supplied with a gas mixture of two-thirds nitrous oxide and one-third oxygen for additional analgesia. The head was shaved, disinfected, fixed in a stereotactic frame and treated with a local analgesic (Lidocaine). The skull was removed around the somatosensory cortex, 3.5 mm off the midline and 0.5 mm posterior to the bregma 8 . The NMR needle was attached to a holder, which was fixed to the skull with bone cement. The needle was then slowly inserted between 1.5 mm and 2 mm into the brain. One animal died during the surgery and for a second one a head trauma was observed during surgery; therefore, no experiments were conducted with those two animals. For the MR measurements, the animal was positioned in an MR-compatible bed with ventilation through a controlled pumped-air facemask. A conventional oval-shaped 20 × 30 mm 2 surface coil was attached horizontally around the implanted needle. Two pairs of needle electrodes were placed between the toes of both anterior paws for peripheral sensory stimulation. Protocol for in vivo MR experiments The position of the needle in the brain after insertion was confirmed via anatomical MR images, acquired with the surface coil (3DGRE matrix size 384 × 384, FOV 45 × 40 mm 2 , eight 1 mm slices, T E = 2.89 ms, T R = 200 ms, FA = 20°). In all functional experiments, a 6 s stimulus (9 Hz, 300 µs pulses, 2.5 mA), interleaved with 24 s rest periods was repeated 20 times. In combination with an initial 20 s rest period with dummy pulses to reach the equilibrium magnetization, this resulted in a total duration of 620 s for one measurement. Conventional functional experiments were performed by transmitting and receiving with the surface coil. A GRE EPI sequence was used (matrix size 64 × 48, FOV 43 × 38 mm 2 , eight 1 mm slices, T E = 9 ms, T R = 1,000 ms, bandwidth 300 kHz). At this stage, one animal had the needle implanted outside the target area, which was confirmed by anatomical images. A second animal did not show any response to the stimulation, neither in conventional EPI nor in the needle experiments, most likely due to an improper anesthesia, which is also a common reason for unsuccessful experiments in conventional fMRI 25 . In three experiments, no valid signal could be measured with the NMR needle, which was found to be caused by mechanical stress at the CMOS–PCB interface during the implantation. This was eliminated in subsequent experiments by an improved probe head design and using a different epoxy glue (EPO-TEK 353ND-T, Epoxy Technology Inc.). In all remaining in vivo experiments, the NMR needle experiments were successful, and all functional experiments were carried out. Here, in seven animals, a change in CBF and blood oxygenation could be observed, while in one animal, no activation could be measured. In the unsuccessful experiment, although near the relevant region, it is most likely that the needle was not close enough to the active brain area. The functional NMR needle experiments were performed using pulse-acquire sequences with 10 µs pulse length without gradients. Different repetition times of T R = 1,000 ms, 50 ms and 5 ms were used corresponding to 600, 12,000 and 120,000 FIDs per experiment. The complex quadrature time-domain signals were sampled at 2 MS s −1 and 16 bit resolution, saved as raw data and evaluated offline after the experiment. Modeling of blood flow and oxygenation changes Neuronal activation triggers a cascade of hemodynamic changes such as increased local CBF and blood oxygenation. To quantify these changes, the relation between the individual FID i and the mean FID mean was assumed to be $${\mathrm{FID}}_i\left( t \right) = \frac{{M_{0,i}}}{{M_{0,{\mathrm{mean}}}}} \times {\mathrm{FID}}_{{\mathrm{mean}}}\left( t \right) \times \exp \left( {\frac{t}{{{\mathrm{\Delta }}T_{2,i}^ \ast }}} \right),$$ where i = 1,…, n is the number of the FID, t is the time elapsed after the excitation pulse and \({\mathrm{\Delta }}T_{2,i}^ \ast\) is the absolute change of \(T_2^ \ast\) of the i th FID. The factor M 0, i / M 0,mean models the relative change of the initial amplitude of each FID i caused by the inflow effect, resulting in \({\mathrm{\Delta }}M_{0,i} = \left( {M_{0,i}/M_{0,{\mathrm{mean}}}} \right) - 1 > 0\) during activation. An increased oxygenation level prolongs the decay of the FID i resulting in $${\mathrm{\Delta }}T_{2,i}^ \ast = 1/{\mathrm{\Delta }}R_{2,i}^ \ast = 1/(R_{2,i}^ \ast - R_{2,\mathrm{mean}}^ \ast ) > 0$$ To estimate Δ M 0, i and \({\mathrm{\Delta }}R_{2,i}^ \ast\) , an exponential fit was applied to $$M_{0,i} \exp \left( {{\mathrm{\Delta }}R_{2,i}^ \ast t} \right) = M_{0,{\mathrm{mean}}} \frac{{{\mathrm{FID}}_i\left( t \right)}}{{{\mathrm{FID}}_{{\mathrm{mean}}}\left( t \right)}}$$ for each individual FID. A two-compartment model consisting of extravascular and intravascular space and corresponding T 1 relaxation times of 2.46 s and 3.16 s (ref. 26 ) was used to simulate the inflow-related signal increase for different excitation flip angles (Supplementary Fig. 6a ). These results do not depend on the chosen fractional blood volume (FBV) and total volume of the two-compartment model and are independent of the repetition time T R up to a certain limit where the saturated blood pool can fully exchange with fresh blood within one T R . This limit is at about T R = 500 ms assuming a baseline CBF of 120 ml per 100 g per min and an FBV of 2% (ref. 9 ). Similar to earlier research 27 , 28 , 29 , randomly oriented cylinders with different radii have been used to model the change of the FID decay rate \(\left( {{\mathrm{\Delta }}R_2^ \ast } \right)\) for different FBV and different changes in local oxygenation, ΔLOX (Supplementary Fig. 6b ). Data analysis All data processing was performed with MATLAB 2017b (The MathWorks Inc.) unless noted otherwise. The volumes of all 3DGRE images were reconstructed by a three-dimensional fast Fourier transform (FFT) without filtering. The B 0 map from Supplementary Fig. 8 was calculated from two 3DGRE sequences with different echo times T E1 and T E2 voxel-by-voxel by extracting the phase difference between the two echoes, unwrapping the calculated volume, and these were converted to hertz by scaling with the difference in the echo times Δ T E . The functional data from the EPI acquisitions were analyzed using Analysis of Functional Neuroimages (v.17.2.02, National Institute of Mental Health) 30 including slice timing correction for the interleaved acquisition and anatomical co-registration. The activation maps were computed on a voxel-by-voxel basis using temporal autocorrelations to calculate the statistically significant maps with thresholds of P < 0.01 (false discovery rate-corrected). Only clusters comprising at least 10 voxels were considered significant. These maps generated the ROIs, which were then used to extract the averaged and concatenated time course in MATLAB. The complex FIDs of the NMR needle were filtered with a Gaussian bandpass filter around the low-IF frequency of 70 kHz with a bandwidth of 5 kHz to remove unwanted noise. The magnitude of the filtered FID was then integrated from 150 µs to 20 ms (4.5 ms for T R = 5 ms) resulting in a single value per FID. Those values were corrected for long-term drifts by applying a second-order polynomial fit over the entire dataset of a 620 s time series and for physiological noise originating from breathing and heart rate by applying narrow-band notch filters at the corresponding frequencies extracted from the measurements with the breathing pad and the pulse oximeter. Additionally, the functional signals were low pass filtered with a 3 Hz Gaussian filter to reduce the noise, since they did not contain any visible stimulation-related features beyond that frequency (Supplementary Fig. 5 ). Calculation of tSNR The tSNR describing the temporal stability of an fMRI signal is the most important figure of merit for the performance of fMRI systems. The measured tSNR values for all signals shown in Fig. 2e are given in Supplementary Table 1 for the raw signal and after each of the signal filtering steps. Although the sensitive volume of 9.8 nl of the needle is about 50 times smaller than a single voxel of the EPI fMRI (530 nl), the tSNR values of the NMR needle at T R = 1,000 ms are only 30% worse than the EPI fMRI results, which were measured over an ROI of 22 voxels with a total volume of 12 µl. The volume-normalized tSNR of the needle for T R = 1,000 ms was about 150 times better than for a single voxel of the reference EPI fMRI experiment. Those results are directly comparable, since both experiments are limited by the short \(T_2^ \ast\) of the tissue. Decreasing T R leads to saturation effects, which therefore results in lower signal amplitudes (Supplementary Fig. 2 ), and, consequently, also in lower raw tSNR for T R = 50 ms and for T R = 5 ms. The second-order baseline correction and the physiological filter produce only a minor tSNR increase, indicating that long-term drifts and physiological noise (mostly heart beat and breathing) do not significantly degrade the NMR needle time course. Since hemodynamic changes are significantly slower than the sampling rates of 20 Hz and 200 Hz, the application of a 3 Hz Gaussian lowpass filter results in an effective tSNR increase of the oversampled physiological signal. The resulting volume-normalized tSNR for T R = 50 ms and T R = 5 ms are 227 and 337 times larger than for the EPI fMRI, respectively. The noise performance of the NMR needle under different conditions was measured to separate different noise sources. The time-domain noise for the needle increased by merely 20% if the needle was surrounded by a water phantom instead of air, confirming that the system noise is dominated by the ohmic resistance of the detection coil and that sample noise is negligible, which is typical for NMR microcoils. The time-domain SNR for the NMR needle immersed in deionized water with T R = 1,000 ms was 330. In the in vivo measurement with T R = 1,000 ms shown in Fig. 2e , the time-domain SNR is reduced to 240, mainly due to the 20% lower proton density of brain tissue compared to water 31 . The additional decrease of the tSNR to 131 compared to the time-domain SNR of 240 is caused by short-term temporal instabilities, which are not corrected by the second-order fit. If those drifts are also corrected, the tSNR increases to 220, which is very close to the time-domain SNR of 240. Statistics and reproducibility From the in vivo experiments, we show representative datasets in Fig. 2b–h and in Supplementary Figs. 2 and 5 . We were able to reproduce similar results for neuronal activation experiments in 7 animals for the NMR needle and in 12 animals for the conventional EPI fMRI. As we observed slightly different response times and amplitudes for different animals, we did not calculate averages for the datasets. The slightly different responses could be caused by several different factors. The most important factor here is the precise location and implantation depth of the needle. Deeper brain areas are known to have a lower magnitude of oxygenation changes 32 . Two other important factors are the anesthesia and the distance between the needle microcoil and nearby vessels and their size. Also, in the EPI results, different degrees of neuronal responses were observed, which are likely caused by an imperfect anesthesia 25 . Reporting Summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data availability The data that support the findings of this study are available from the corresponding authors upon request. | Physics |
https://dx.doi.org/10.1038/s41586-022-05448-9 | https://medicalxpress.com/news/2022-12-genomic-variants-chip.html | New genomic variants associated with CHIP identified | A team of researchers at Regeneron Pharmaceuticals has identified new genomic variants associated with clonal hematopoiesis of indeterminate potential (CHIP). In their paper published in the journal Nature, the group describes how they used exome-wide and genome-wide association analyses to study differences in the blood of some people with somatic mutations. Nature has also published a Research Highlights piece in the same journal issue, discussing the work done by the New York team. Hematopoiesis is a process that results in the formation of cellular blood components. And clonal hematopoiesis is the part of the process that is involved in the development of cell lineages. The importance of the overall process is highlighted by the fact that every person produces approximately 300 billion new blood cells every single day of their life. Prior research has suggested that there are variants associated with clonal hematopoiesis of indeterminate potential in certain people—each of which can have a unique impact. In this new effort, the team at Regeneron sought to find some of them by studying information held in very large datasets, such as the UK Biobank and the Geisinger MyCode Community Health Initiative. To find the variants they were after, the researchers focused their search efforts on 23 genes that have already been associated with CHIP. By searching through data on 628,388 individuals, they were able to identify 40,208 carriers of at least one variant associated with CHIP. They then conducted exome-wide and genome-wide studies of the carriers they had identified. In so doing, they were able to identify 24 loci—21 of which had not been seen before. The researchers also found that they were able to identify some variants that could be associated with clonal hematopoiesis and the length of telomeres in certain individuals. In another part of their study, the team analyzed health traits of people listed in the UK Biobank looking for associations between people who had CHIP variants and other issues. In so doing, they found associations between people who had clonal hematopoiesis variants and diseases such as COVID-19, heart problems, obesity and problems clearing infections of various types. They also found associations between individuals with CHIP and development of cancerous tumors and myeloid leukemias. | Michael D. Kessler et al, Common and rare variant associations with clonal haematopoiesis phenotypes, Nature (2022). DOI: 10.1038/s41586-022-05448-9 Kirsty Minton, CHIPping away at the genetic aetiology of clonal haematopoiesis, Nature Reviews Genetics (2022). DOI: 10.1038/s41576-022-00565-7 Journal information: Nature , Nature Reviews Genetics | 10.1038/s41586-022-05448-9 | Abstract Clonal haematopoiesis involves the expansion of certain blood cell lineages and has been associated with ageing and adverse health outcomes 1 , 2 , 3 , 4 , 5 . Here we use exome sequence data on 628,388 individuals to identify 40,208 carriers of clonal haematopoiesis of indeterminate potential (CHIP). Using genome-wide and exome-wide association analyses, we identify 24 loci (21 of which are novel) where germline genetic variation influences predisposition to CHIP, including missense variants in the lymphocytic antigen coding gene LY75 , which are associated with reduced incidence of CHIP. We also identify novel rare variant associations with clonal haematopoiesis and telomere length. Analysis of 5,041 health traits from the UK Biobank (UKB) found relationships between CHIP and severe COVID-19 outcomes, cardiovascular disease, haematologic traits, malignancy, smoking, obesity, infection and all-cause mortality. Longitudinal and Mendelian randomization analyses revealed that CHIP is associated with solid cancers, including non-melanoma skin cancer and lung cancer, and that CHIP linked to DNMT3A is associated with the subsequent development of myeloid but not lymphoid leukaemias. Additionally, contrary to previous findings from the initial 50,000 UKB exomes 6 , our results in the full sample do not support a role for IL-6 inhibition in reducing the risk of cardiovascular disease among CHIP carriers. Our findings demonstrate that CHIP represents a complex set of heterogeneous phenotypes with shared and unique germline genetic causes and varied clinical implications. Main As humans age, somatic alterations accrue in the DNA of haematopoietic stem cells (HSCs) due to mitotic errors and DNA damage. Alterations that confer a selective growth advantage can lead to the expansion of particular cell lineages, a phenomenon called clonal haematopoiesis. The presence of clonal haematopoiesis has been associated with an increased risk of haematological neoplasms, cytopaenias, cardiovascular disease (CVD), infection and all-cause mortality 1 , 2 , 3 , 4 , 5 . For this reason, identifying germline causes of clonal haematopoiesis has the potential to improve our understanding of initiating events in the development of these common diseases. Large-scale studies of the germline causes of clonal haematopoiesis have used samples from the UKB and other large cohorts, but those studies have been limited mostly to clonal haematopoiesis phenotypes that can be assessed using single nucleotide polymorphism (SNP) array genotype data, such as mosaic chromosomal alternations (mCA) and mosaic loss of sex chromosomes 4 , 7 , 8 (mLOX and mLOY). Identifying individuals with CHIP, which is defined by somatic protein-altering mutations in genes that are recurrently mutated in clonal haematopoiesis, requires sequencing of blood 1 , 2 . Once a clone has expanded sufficiently, the somatic variants from this clone can be captured along with germline variants by exome sequencing. Since exome sequencing captures protein-altering variants, its large-scale application enables the detection of readily interpretable rare variant association signals, and can elucidate critical genes and pathways and potential therapeutic targeting 9 , 10 . So far, the largest genetic association study of CHIP has included 3,831 CHIP mutation carriers in a sample of 65,405 individuals and has identified four common variant loci 11 . Here, we use exome sequencing data to characterize CHIP status in 454,803 UKB 10 and 173,585 Geisinger MyCode Community Health Initiative (GHS) participants. We then conduct a common variant genome-wide association study (GWAS) and rare variant and gene burden exome-wide association study (ExWAS) of CHIP by leveraging 27,331 CHIP mutation carriers from the UKB. We perform a replication analysis using 12,877 CHIP mutation carriers from the GHS cohort. To identify germline predictors of specific clonal haematopoiesis driver mutations, we also conduct GWAS and ExWAS in carriers of CHIP mutations from individual CHIP genes. We then compare genetic association findings for CHIP to those from analyses of other clonal haematopoiesis phenotypes determined from somatic alterations in the blood, including mCA, mLOX, mLOY and telomere length. Although GWAS of these non-CHIP clonal haematopoiesis phenotypes have been conducted 4 , 7 , 12 , none have evaluated the effect of rare variation. The ExWAS we perform here represents the first systematic large-scale exploration of the effect of rare variants on the genetic susceptibility of these phenotypes. Finally, we examine the clinical consequences of somatic CHIP mutations and germline predictors of CHIP in several ways. We first conduct a PheWAS 13 of germline predictors of CHIP to understand their biological functions, and test cross-sectional phenotype associations of CHIP carrier status across 5,194 traits in the UKB. We then test the risk of incident cancer, CVD and all-cause mortality among specific CHIP gene mutation carriers and use Mendelian randomization to test for evidence of causal associations between CHIP and phenotypes of interest. Calling CHIP We used exome sequencing data from 454,803 and 173,585 individuals from the UKB and GHS cohorts, respectively, to generate large callsets of CHIP carrier status ( Methods ). In brief, we called somatic mutations using Mutect2 in a pipeline that included custom QC filtering (Extended Data Fig. 1a ), and ultimately restricted our analysis to 23 well defined and recurrent CHIP-associated genes. This focused analysis identified 29,669 variants across 27,331 individuals in the UKB (6%), and 14,766 variants across 12,877 individuals in the GHS (7.4%). DNMT3A , TET2 , ASXL1 , PPM1D and TP53 were the most commonly mutated genes in both cohorts (Extended Data Fig. 2a ). Although the GHS cohort had a wider age range, and therefore a larger number of older individuals, the prevalence by age was similar across cohorts, and reached approximately 15% by 75 years of age (Extended Data Fig. 1b,c ). Prevalence of CHIP gene-specific mutations was consistent with recurrence patterns, with mutations in the most commonly mutated CHIP genes beginning to increase in prevalence at younger ages (Extended Data Fig. 1d,e and Supplementary Note 1 ). Somatic mutations within the IDH2 and SRSF2 genes co-occurred significantly more frequently than expected in both the UKB and GHS cohorts, whereas DNMT3A mutations co-occurred less frequently with other mutations than expected (Extended Data Fig. 2b,c and Supplementary Table 1 ). Among individuals with multiple CHIP mutations (Supplementary Note 2 and Supplementary Fig. 1 ), JAK2 mutations consistently had the highest variant allele fraction (VAF) (Supplementary Fig. 1b ). CHIP demographics Compared with controls, CHIP carriers in both the UKB and GHS cohorts were older and more likely to be heavy smokers, consistent with previous studies 11 (Table 1 ). Although our cohorts were predominantly comprised of European ancestry individuals, the prevalence of CHIP was similar across all ancestries (Supplementary Fig. 2 ). In multivariate logistic regression models, each additional year of age was strongly associated with an increased risk of CHIP in the UKB (odds ratio [range] = 1.08 [1.077–1.082], P < 10 −300 ) and GHS (odds ratio = 1.06 [1.057–1.063], P < 10 −300 ), and heavy smoking was strongly associated with CHIP carrier status in both UKB (odds ratio = 1.17 [1.14–1.21], P = 7.32 × 10 −24 ) and GHS (odds ratio = 1.24 [1.10–1.41], P = 6.3 × 10 −4 ). Overall, our results suggest that the prevalence of CHIP doubles every 9–12 years of life. These associations with age and smoking were stronger when restricting to high-VAF (≥0.1) CHIP carriers. In our multivariate modelling, women were significantly more likely to be CHIP mutation carriers than men in the UKB (odds ratio = 1.08 [1.05–1.11], P = 6.01 × 10 −7 ), but not in the GHS (odds ratio = 1.01 [0.93–1.11, P = 0.77]). These associations were consistent when restricting to high-VAF CHIP carriers, although the risk of high-VAF CHIP was not significantly greater in women in the UKB (odds ratio = 1.035 [0.99–1.08], P = 0.126). Table 1 Descriptive statistics for CHIP mutation carriers Full size table Genetic association with CHIP carrier status We first conducted genetic association analyses in the UKB cohort to identify germline loci associated with the risk of developing CHIP. In the common variant (minor allele frequency (MAF) > 0.5%) GWAS, which included 25,657 cases and 342,869 controls with European ancestry, we identified 24 loci (21 novel loci) harbouring 57 independently associated variants (Fig. 1 and Supplementary Table 2 ). To confirm these signals, we conducted a replication analysis in 9,523 CHIP cases and 105,502 controls of European ancestry from the GHS cohort. We estimated that we had sufficient statistical power in the GHS to detect 19.99 true and directionally consistent associations across lead SNPs from the 24 loci we identified in the UKB and achieved nominally significant ( P < 0.05) replication for 15 SNPs (Supplementary Table 2 ). We used conditional analysis and statistical fine-mapping to further evaluate the independence of our genome-wide associations and found results to be consistent across methods (Extended Data Fig. 3 , Supplementary Note 3 , Supplementary Tables 3 – 6 and Supplementary Fig. 3 ). Fig. 1: GWAS of CHIP. Manhattan plot showing results from a genome-wide association analysis of CHIP. Twenty-four loci reach genome-wide significance ( P ≤ 5 × 10 −8 , dashed line), and top-associated variants per locus are labelled with biologically relevant genes. Three of these loci have been previously identified (black), whereas 21 represent novel associations (red). Loci with suggestive signal ( P ≤ 5 × 10 −7 ) are labelled in grey. Association models were run with age, age 2 , sex and age × sex, and 10 ancestry-informative principal components as covariates. P -values are uncorrected and are from two-sided tests performed using approximate Firth logistic regression. Full size image We next sought to identify rare germline variants associated with CHIP. Since the CHIP phenotype is based on the presence of rare somatic variants in recurrently mutated genes, rare germline variants potentially misclassified as somatic can lead to false association signals. To address potential misclassification, we evaluated median VAF and association with age for each rare germline variant or gene burden associated with CHIP. We also conditioned these rare variant analyses on independent common variant signals to address confounding due to linkage disequilibrium (LD) (Supplementary Note 4 ). Ultimately, we identified a single rare germline frameshift variant in the CHEK2 gene that was significantly associated with CHIP (odds ratio = 2.22 [1.89–2.61], P = 8.04 × 10 −22 ; Supplementary Table 7 ), remained so after conditioning on common variant signals (odds ratio = 2.90 [1.93–4.34], P = 2.40 × 10 −7 ), and replicated in the GHS (odds ratio = 1.56 [1.19–2.04], P = 1.22 × 10 −3 ). The two cancer-associated genes ATM and CHEK2 were associated with an increased risk of CHIP via rare variant gene burden testing (Supplementary Table 8 ), and we also found a significant gene burden association between rare loss of function (and missense) variants in the telomere maintenance and DNA replication associated gene CTC1 and an increased risk of CHIP (odds ratio = 1.55 [1.32–1.81], P = 5.24 × 10 −8 ). Of these three gene burden associations, the ATM and CHEK2 signals were replicated in the GHS ( P = 8.22 × 10 −5 and P = 0.03, respectively), and VAF and age-association calculations suggested that all three of these gene burden signals were driven by germline variation. We also performed genome-wide association analyses in individuals of non-European ancestral background (Supplementary Note 5 and Supplementary Table 9 ). For each germline variant associated with CHIP and prioritized by clumping and thresholding, conditional analysis or fine-mapping (see Methods ), we queried its associations across 937 binary and quantitative health traits from the UKB for which we have previously performed genetic association analysis 10 (Supplementary Table 10 ). Overall, the traits with significant associations consisted predominantly of blood measures (that is, cells counts and biomarker levels), anthropometric measures related to body size, autoimmune phenotypes and respiratory measures. SNPs with the largest number of significant phenotypic associations included those at the HLA , TP53 , ZFP36L2 and THADA, CD164 and MYB loci (Extended Data Fig. 4 ). Whereas associations with blood cell counts and biomarker levels are probably the direct result of expansion of individual cell lineages in blood, association with autoimmune phenotypes could reflect the consequences of disrupted immune system differentiation related to clonal haematopoiesis. Analyses of individual CHIP gene mutations To identify CHIP subtype-specific risk variants, we defined gene-specific CHIP phenotypes for each of the eight most commonly mutated CHIP genes. For each subtype, we selected individuals with mutations in one of the eight genes and no mutations in any of the other genes used to define CHIP. We then conducted genetic association analyses comparing these single CHIP gene carriers to CHIP-free controls, with replication in the GHS, and observed shared, unique, and opposing effects of associated loci on CHIP subtypes, including 8 genome-wide significant loci that were not significant in our overall analysis of CHIP (Fig. 2a , Extended Data Fig. 5 and Supplementary Tables 11 – 19 ). Fig. 2: Germline effect size comparisons across CHIP and Forest plots of PARP1 and LY75 missense variants. a , Using results from CHIP gene-specific association analyses, effect sizes of index SNPs are compared across CHIP subtypes. SNPs were chosen as those that were independent on the basis of clumping and thresholding (with some refinement based on our conditionally independent variant list) and genome-wide significant in at least one association with CHIP or a CHIP subtype. Certain loci showed notably different effects across CHIP subtypes, as seen at the CD164 locus, which was associated with DNMT3A CHIP and ASXL1 CHIP but not TET2 CHIP, and the TCL1A locus, which was associated with increased risk of DNMT3A CHIP but reduced risk of other CHIP subtypes (blue rectangles). b , Forest plots are shown reflecting the protective associations of a PARP1 missense variant (rs1136410-G) and two LY75 missense variants (rs78446341-A, rs147820690-T) with our DNMT3A CHIP phenotype in the UKB and GHS cohorts. Centre points represent odds ratios as estimated by approximate Firth logistic regression, with errors bars representing 95% confidence intervals. P -values are uncorrected and reflect two-sided tests. Numbers below the cases and controls columns represent counts of individuals with homozygote reference, heterozygote and homozygous alternative genotypes, respectively. Full size image DNMT3A , which was the most commonly mutated gene in the overall CHIP phenotype, had the largest number of significantly associated loci ( n = 23), most of which overlapped with the overall CHIP association signals. Six loci achieved genome-wide significance in our DNMT3A CHIP analysis that were not significant in our overall analysis ( RABIF , TSC22D2 , ABCC5 , MYB , FLT3 and TCL1A ; Extended Data Fig. 5 ). Although most loci harboured variants that increased CHIP risk, two exceptions are noteworthy (Fig. 2b ). At the PARP1 locus on chromosome 1, a tightly linked block of around 30 variants (29 in the 95% credible set from fine-mapping; Supplementary Table 6 ) with an alternate allele frequency (AAF) of 0.15 was associated with reduced risk of DNMT3A CHIP (odds ratio = 0.87 [0.84–0.90], P = 2.70 × 10 −17 ). PARP1 has a role in DNA damage repair, and many variants in this block have been identified across multiple transcriptomic studies of blood as PARP1 expression quantitative trait loci (eQTLs) that associate with reduced PARP1 gene expression 14 , 15 , 16 , 17 . Furthermore, a missense variant (rs1136410-G, V762A) that is predicted as likely to be damaging (combined annotation dependent depletion (CADD) score = 27.9) is a part of this LD block, and has recently been reported to associate with improved prognosis and survival in myelodysplastic syndromes 18 (MDS). At a locus on chromosome 2, rs78446341 (P1247L in LY75 ) was associated with reduced risk of DNMT3A CHIP (odds ratio = 0.78 [0.72–0.84], P = 3.70 × 10 −10 ), and was prioritized by fine-mapping (Extended Data Fig. 3 ). LY75 features lymphocyte-specific expression (Supplementary Fig. 4a ), and is thought to be involved in antigen presentation and lymphocyte proliferation 19 . We also identified a second rare (AAF = 0.002) missense variant (rs147820690-T, G525E) that associated with reduced risk of DNMT3A CHIP at close to genome-wide significance (odds ratio = 0.48 [0.36–0.63], P = 1.15 × 10 −7 ). This variant was predicted as likely to be damaging (CADD = 23.6) and remains associated (odds ratio = 0.63 [0.51–0.77], P = 4.80 × 10 −6 ) when conditioning on common variant signal in this locus (that is, this rare variant signal is independent of the common variant signal in this locus). This variant was also prioritized by fine-mapping (Extended Data Fig. 3 and Methods for jointly fine-mapping common and rare variants). Finally, these signals in PARP1 and LY75 replicated in the GHS (Fig. 2b ). Among loci associated with multiple CHIP subtypes (Supplementary Note 6 ), we observed genome-wide significant association signals at the TCL1A locus that were not present in the overall CHIP analysis. This locus is notable because it exhibited genome-wide significant effects in opposing directions across CHIP subtypes (Extended Data Figs. 2a and 5 and Supplementary Table 20 ), with lead SNPs (for example, rs2887399-T, rs11846938-G and rs2296311-A) at the locus associated with an increased risk of DNMT3A CHIP (odds ratio = 1.14 [1.11–1.17], P = 2.13 × 10 −20 ) but a reduced risk of TET2 CHIP (odds ratio = 0.75 [0.71–0.80], P = 9.14 × 10 −22 ) and ASXL1 CHIP (odds ratio = 0.70 [0.65–0.76], P = 8.59 × 10 −18 ). Effect estimates from the other five CHIP gene-specific association analyses were also consistent with protective effects. This is consistent with findings from a recent genetic association study of CHIP in the TOPMed cohort 11 , which identified a genome-wide significant positive association of the TCL1A locus and DNMT3A CHIP as well as a nominally significant opposing signal for TET2 CHIP. Additionally, the DNMT3A CHIP-increasing allele has been found to reduce the risk of mLOY in a recent GWAS 7 . This observation suggests that DNMT3A CHIP is distinct among clonal haematopoietic subtypes with regard to the genetic influence of the TCL1A locus, which may relate to the fact that TCL1A has been reported to directly interact with and inactivate DNMT3A 20 . CHIP and mosaic chromosomal alterations To evaluate the relationship between CHIP and other forms of somatic alterations of the blood, we used phenotype information on other types of clonal haematopoiesis that are available for UKB participants 4 , 7 , 8 , 12 . We first evaluated the phenotypic overlap between CHIP and mLOY, mLOX and autosomal mosaic chromosomal alterations (mCAaut). CHIP is distinct from mCA phenotypes (mCAaut, mLOX and mLOY), with more than 80% of CHIP carriers having no identified mCAs (Supplementary Fig. 4b ). Furthermore, having an mCA is not significantly associated with being a CHIP carrier after adjusting for age, sex and smoking status (odds ratio = 1.02, P = 0.27). Carriers of only a single clonal haematopoiesis driver (that is, CHIP, mLOY, mLOX or mCAaut) were younger on average than those with multiple clonal haematopoiesis lesions, and mCAaut and CHIP carriers were youngest among single clonal haematopoiesis phenotype carriers (Supplementary Fig. 4c ). We then conducted GWAS and ExWAS analyses of these somatic alteration phenotypes and evaluated the germline genetic contributions shared between CHIP and these traits (Supplementary Fig. 5 and Supplementary Tables 21 – 27 ). Genome-wide genetic correlation ( r g ) 21 , 22 was nominally significant between CHIP and mLOY ( r g = 0.27, P = 0.014 (uncorrected); Supplementary Table 21 ). Notably, variants at 4 loci (marked by the genes ATM, LY75, CD164 and GSDMC ) showed similar associations with both CHIP and mLOY, whereas variants at the SETBP1 locus were negatively associated with CHIP and positively associated with mLOY. These comparisons suggest that despite being distinct clonal haematopoietic phenotypes, CHIP and mLOY share multiple germline genetic risk factors. Although the common variant association analyses of these other somatic alteration phenotypes were undertaken for the purpose of comparing to CHIP, and our results are consistent with recent published associations for these non-CHIP UKB somatic alteration phenotypes 4 , 7 , 8 , we also identified novel rare variant and gene burden associations via ExWAS analyses (Supplementary Note 7 , Supplementary Tables 22 – 27 and Supplementary Fig. 6 ). We also extended our ExWAS analysis to telomere length and identified multiple novel rare variant associations (Supplementary Note 8 and Supplementary Tables 28 – 30 ). Phenotypic associations with CHIP Clonal haematopoiesis has been associated with an increased risk of haematologic malignancy and CVD, as well as other health outcomes including all-cause mortality and susceptibility to infection 3 , 4 , 23 , 24 . To test for expected as well as potentially novel associations, we performed cross-sectional association analyses across 5,041 traits (2,640 binary and 2,401 quantitative traits) from the UKB, curated as part of our efforts for the UKB Exome Sequencing Consortium. We performed Firth penalized logistic regression using CHIP gene mutation carrier status (that is, whether an individual had a mutation in our callset within a specific CHIP gene) as the binary outcome for 22 of the 23 CHIP genes in our callset (counts were too low for CSF3R ; Methods ), with age, sex and ten genetic principal components as covariates. Our results are consistent with previous findings, with the majority of associated phenotypes deriving from cardiovascular, haematologic, neoplastic, infectious, renal and/or smoking-related causes (Fig. 3 , Supplementary Fig. 7 and Supplementary Table 31 ). Fig. 3: Phenome association profiles per CHIP subtype. Profiles are shown for each CHIP gene subtype reflecting phenome-wide association results. The y -axis (concentric circles) represents the proportion of phenotypes within a trait category that were nominally associated ( P ≤ 0.05) with carrier status of the CHIP gene. A CHIP gene had to have at least one disease category with the proportion of associated phenotypes ≥ 0.2 to be included in the figure. As expected, haematological traits show the largest proportion of phenotypic trait associations overall. The largest number of cancer associations are seen for DNMT3A CHIP, whereas JAK2 CHIP shows the highest proportion of cardiovascular associations. Respiratory associations are most pronounced for ASXL1 CHIP. SUZ12 CHIP shows a unique profile across CHIP subtypes, with a higher proportion of ophthalmological and endocrine associations. Association models were run with age, age 2 , sex and age × sex, and ten ancestry-informative principal components as covariates. Full size image ASXL1 CHIP was associated with the largest number and widest range of traits, and many of these associations traced to correlates of smoking. SUZ12 CHIP showed a distinct association profile amongst CHIP genes, with a larger proportion of associations in endocrine and ophthalmologic traits than other CHIP genes. Many traits showed associations with DNMT3A CHIP and TET2 CHIP that were in opposing directions, including white blood cell count, platelet count and neutrophil count, which were all positively associated with DNMT3A CHIP and negatively associated with TET2 CHIP. These results are consistent with functional differences in the haematopoietic phenotypes of DNMT3A - and TET2 -knockout mice 25 . Notably, body mass index (BMI) and fat percentage were negatively associated with DNMT3A CHIP and other leukaemogenic CHIP mutations (for example, JAK2 , CALR and MPL ), but are positively associated with other CHIP subtypes (for example, TET2 and ASXL1 ). We also observed significant associations between JAK2 mutations and gout, which may reflect the increased uric acid production that can accompany haematopoiesis 26 and/or renal disease 27 , or even uric acid-independent associations identified between anaemia and gout 28 . Given recent reports that clonal haematopoiesis is associated with an increased risk of COVID-19 and other infections 4 , 29 , we also tested for an association between CHIP and COVID-19 infection in the UKB cohort 30 . When restricting to CHIP carriers with VAF ≥ 10% (Supplementary Note 9 ), we found that CHIP carrier status was significantly associated with COVID-19 hospitalization (odds ratio = 1.26 [1.07–1.47], P = 4.5 × 10 −3 ) and severe COVID-19 infection (odds ratio = 1.55 [1.19–1.99], P = 8.5 × 10 −4 ) in logistic regression models that excluded individuals with any previous blood cancers and that adjusted for age, sex, smoking, BMI, type 2 diabetes, active malignancy, and five genetic principal components. Analyses at the CHIP subtype level suggested that PPM1D carriers may be at elevated risk of severe COVID-19 (odds ratio = 5.42 [1.89–12.2], P = 2.8 × 10 −4 ; Supplementary Note 9 ). Longitudinal disease risk among CHIP carriers Given the confounding that can bias cross-sectional association analyses, we performed survival analyses to evaluate whether individuals with CHIP at the time of enrolment and blood sampling in the UKB were at an increased risk of subsequent CVD, cancer and all-cause mortality. To do this, we generated aggregate longitudinal phenotypes of CVD, lymphoid cancer, myeloid cancer, lung cancer, breast cancer, prostate cancer, colon cancer and overall survival (that is, any death). Because prior longitudinal studies of CHIP and the risk of many of these outcomes have focused on high-VAF CHIP, we focused on CHIP carriers with VAF ≥ 0.10 for these analyses. To complement these longitudinal analyses, we used Mendelian randomization to evaluate the relationship between CHIP and subsequent disease (Extended Data Fig. 6a , Supplementary Note 10 and Supplementary Table 32 ). We observed a significantly increased risk of CVD in CHIP carriers (hazard ratio = 1.11 [1.03–1.19], P = 4.2 × 10 −3 ), which was driven by TET2 CHIP (hazard ratio = 1.31 [1.14–1.51], P = 1.3 × 10 −4 ; Supplementary Fig. 8a ). However, this risk estimate is lower than the hazard ratio of 1.59 recently reported by Bick et al. 6 in an analysis of CHIP from the first 50,000 UKB participants (hereafter referred to as the 50k UKB subset) with exome sequencing data available. Therefore, we restricted our analysis to the 50,000 individuals from the previous study and found that the estimated hazard ratio is indeed higher in this subset (hazard ratio = 1.30 [1.06–1.59], P = 0.013; Supplementary Fig. 8b ). Bick et al. also observed a cardio-protective effect of IL6R rs2228145-C (a genetic proxy for IL-6 receptor inhibition) among CHIP carriers in the 50k UKB subset, so we repeated that analysis in both the 50k UKB subset and the full UKB cohort ( n = 430,924 in these analyses). We observed the same CHIP-specific protective IL6R effect in the 50k UKB subset as previously reported (hazard ratio = 0.60 [0.40–0.89], P = 0.012), however we did not find any IL6R effect in the full cohort (hazard ratio = 0.99 [0.91–1.07], P = 0.784, n = 430,924; Extended Data Fig. 7a–d ). These results were consistent when varying which CHIP mutations we used to define CHIP case status, as well as when using different VAF thresholds and a variety of CVD endpoint composites ( Methods ). We did not find any association between CHIP and CVD, nor a CHIP-specific protective IL6R effect, when repeating this analysis in the GHS cohort (Supplementary Figs. 8d and 9a, b ). Furthermore, we did not find evidence for a casual association between CHIP and CVD when using a two-sample Mendelian randomization approach (Supplementary Note 10 , Supplementary Fig. 10 and Supplementary Table 32 ). We next tested whether CHIP carriers are at an increased risk of haematologic and solid cancers, and whether risk differed by CHIP mutational subtype for the three most common CHIP genes (that is, DNMT3A , TET2 and ASXL1 ; Extended Data Figs. 7 – 9 and Supplementary Figs. 11 – 14 ). To control for the possibility that toxic chemotherapeutic treatment for previous cancers might drive the development of CHIP mutations 31 and/or otherwise confound association analyses, we performed all analyses after excluding individuals with any diagnoses of cancer prior to DNA collection. As expected, we found CHIP carriers with VAF ≥ 0.10 to be at a significantly elevated risk of developing any blood cancer (hazard ratio = 3.88 [3.46–4.36], P = 9.10 × 10 −117 ; Supplementary Fig. 11a ), and we identified similarly elevated risk when replicating these analyses in the GHS (Supplementary Fig. 11d ). We also estimated the risk of CHIP on neoplastic myeloid subtypes, including acute myeloid leukaemia (AML), MDS and myeloproliferative neoplasms (MPN), and found that high-VAF CHIP carriers have more than 23-fold increased risk of acquiring an MPN (hazard ratio = 23.11 [17.63–30.29], P = 1.60 × 10 −114 ) (Extended Data Fig. 8 ). As expected, we identified a significant association between myeloid leukaemia and CHIP by Mendelian randomization (Supplementary Note 10 , Supplementary Fig. 12 and Supplementary Table 32 ). We then tested whether CHIP carriers had an increased risk of developing solid tumours, and found that high-VAF carriers are at significantly increased risk of developing lung cancer (hazard ratio = 1.64 [1.42–1.90], P = 1.10 × 10 −11 ), and more modest increased risk of developing prostate cancer (hazard ratio = 1.18 [1.05–1.32], P = 5.30 × 10 −3 ) and non-melanoma skin cancer (hazard ratio = 1.14 [1.04–1.24], P = 4.7 × 10 −3 ; Fig. 4 and Supplementary Fig. 13 ). We also observed a non-significant increased risk of developing breast cancer (hazard ratio = 1.14 [0.99–1.31], P = 0.062) and no increase in risk for the development of colon cancer (hazard ratio = 0.95 [0.78–1.15], P = 0.59; Supplementary Fig. 13 ). Models estimating event risk on the basis of CHIP mutational subtype (for example, DNMT3A CHIP) suggest that these associations with prostate and breast cancer are driven primarily by DNMT3A mutations. Only the association with lung cancer was replicated in the GHS (Fig. 13e ), although sample sizes were limited for the analyses in the GHS owing to how the biobank data were ascertained ( Methods ). Fig. 4: Increased risk of lung cancer among CHIP carriers. a , Forest plot and table featuring hazard ratio estimates from Cox proportional hazard models of the risk lung cancer among CHIP carriers. Error bars represent a 95% confidence interval. Associations are similar across common CHIP subtypes, as well as among CHIP carriers with lower VAF (≥2%). Models are adjusted for sex, low density lipoprotein, high density lipoprotein, smoking status, pack years, BMI, essential primary hypertension, type 2 diabetes mellitus, and 10 genetic principal components specific to a European ancestral background. HR, hazard ratio. UKB 450K, the 450,00-participant full UKB dataset. DNMT3A+ represents subjects with DNMT3A CHIP and at least one other type of CHIP mutation. b , Estimated associations via four Mendelian randomization methods between CHIP and lung cancer. Each point represents one of 29 instrumental variables (that is, conditionally independent SNPs) that were identified in the UKB cohort as associated with CHIP. The x -axis shows the effect estimate (beta) of the SNP on CHIP in the UKB cohort, and the y -axis shows the effect estimate (beta) of the SNP on lung cancer in the GHS cohort. The slope of each regression line represents the effect size estimated by respective methods. IVW, inverse variance weighted. Full size image Given the strong associations between CHIP and both blood and lung cancers, and the associations between smoking and both CHIP and lung cancer, we performed additional analyses stratified by smoking status to test whether these associations were driven by smoking and merely marked by CHIP mutations. Although smoking status is difficult to ascertain, we used an inclusive ‘ever smoker’ definition to minimize the likelihood that individuals labelled as non-smokers had engaged in any smoking ( Methods ). High-VAF CHIP carriers had an increased risk of developing blood cancers in both smokers (hazard ratio = 3.95 [3.25–4.78], P = 2.80 × 10 −44 ) and non-smokers (hazard ratio = 3.97 [3.43–4.58], P = 1.10 × 10 −77 ; Supplementary Fig. 14a, b ). Notably, lung cancer risk for high-VAF CHIP carriers was significantly elevated among both smokers (hazard ratio = 1.67 [1.41–1.97], P = 1.5 × 10 −9 ) and non-smokers (hazard ratio = 2.02 [1.53–2.67], P = 8.30 × 10 −7 ; Extended Data Fig. 9a,b ). These associations were driven by DNMT3A and ASXL1 CHIP carriers, with both estimated to have elevated lung cancer risk in both smokers and non-smokers. We replicated the association between CHIP carrier status and lung cancer in both smokers and non-smokers in the GHS (Extended Data Fig. 9c,d ). Overall, these models suggest that CHIP mutation carriers are at an elevated risk of both blood cancer and lung cancer, independent of smoking status. We also found support for a causal association between CHIP and lung cancer (inverse variance weighted odds ratio (OR IVW ) = 1.55 [1.34–1.80], P = 8.90 × 10 −9 ; Fig. 4 and Extended Data Table 1 ), as well as more modest support for causal associations between CHIP and melanoma (OR IVW = 1.39 [1.13–1.1.71], P = 0.0021), CHIP and non-melanoma skin cancer (OR IVW = 1.26 [1.13–1.41], P = 5.30 × 10 −5 ), CHIP and prostate cancer (OR IVW = 1.20 [1.03–1.1.39], P = 0.017), and CHIP and breast cancer (1.17 [1.04–1.31], P = 0.01), when performing Mendelian randomization (Extended Data Fig. 6a , Supplementary Note 10 and Supplementary Table 32 ). Although there is a concern that variants predisposing to CHIP via cancer-associated pathways (for example, telomere biology, DNA damage repair and cell cycle regulation) may confound these associations via horizontal pleiotropy, Egger-based Mendelian randomization methods that account for this bias by fitting a non-zero intercept provided additional support for these associations. Finally, the risk of death from any cause was significantly elevated among high-VAF CHIP carriers (hazard ratio = 1.27 [1.18–1.36], P = 2.70 × 10 −11 ), and was similar across DNMT3A , TET2 and ASXL1 CHIP subtypes (Extended Data Fig. 6b ). In this study, we present the largest assessment to date of individuals with CHIP mutation carrier information, as well as the use of these calls to identify novel common and rare variant loci associated with CHIP and CHIP subtypes. These loci, which have shared, unique and opposing effects on the risk of developing different types of CHIP and other somatic alterations of the blood, highlight the fact that germline variants can predispose to clonal expansions, and that CHIP encapsulates a complex set of heterogeneous phenotypes. We further show that the genetic aetiology of CHIP is reflected in its clinical consequences, as the risk of various clinical conditions is differentially associated across CHIP gene mutations. The new loci identified in this study provide a foundation on which to investigate the biological mechanisms that lead to specific features of CHIP. For example, among CHIP-associated loci, variants in the TCL1A locus that are associated with an increase in the risk of DNMT3A CHIP have the opposite effect on the risk of all other CHIP and clonal haematopoiesis subtypes. Coupled with recent findings that link the role of TCL1A in mLOY to lymphocytes 7 (for example, B cells), our results further suggest TCL1A as a critical mediator of clonal haematopoiesis as well as clonal haematopoiesis subtype-specific differences. Several novel loci associated with DNMT3A CHIP harbour genes that are potential targets for the development of new treatments to prevent or slow the expansion of CHIP clones. Both PARP1 and LY75 contain missense variants associated with reduced risk of CHIP and of DNMT3A CHIP specifically. The variants in the PARP1 locus are significantly associated with reduced PARP1 gene expression in whole blood 32 ( P ≤ 1 × 10 −13 ), and the V762A missense variant (rs1136410-G) has been recently reported to associate with improved prognosis and survival in MDS 18 . Given the well-established role of PARP1 in DNA repair 33 , and that a recent CRISPR screen study in zebrafish identified PARP1 inhibition as a selective killer of TET2 mutant haematopoietic stem cells 34 , it seems plausible that a therapeutic strategy that inhibits PARP1 might be viable for the antagonization of CHIP clone expansion. Furthermore, PARP1 -inhibiting drugs are already approved for use in the treatment of BRCA-mutant cancers 35 . Conversely, PARP1 inhibition is known to cause haematologic toxicity and to increase the risk of treatment related haematologic malignancy 36 . Therefore, further research is needed to test whether PARP1 inhibition may be appropriate for use in antagonizing the expansion of CHIP clones, and whether any effect is clonal haematopoiesis subtype-specific. The more common LY75 missense variant (rs78446341-A, P1247L) is located in the extracellular domain of lymphocytic antigen 75 (also known as DEC-205 or CD205), and has a role in antigenic capture, processing and presentation 37 . The rarer LY75 missense variant (rs147820690-T, G525E) is located in a C-type lectin domain and reported to interact directly with this receptor’s ligand. LY75 is expressed predominantly in haematopoietic-derived cells 37 , 38 (and particularly dendritic cells), and its ablation impairs T cell proliferation and response to antigen challenge 19 . The protective associations with this variant that we identified appear to be most pronounced for DNMT3A CHIP and mLOY, and highlight LY75 as a potential therapeutic target for the antagonization of clonal haematopoiesis in general. Although most of the phenotypic associations we observe in our cross-sectional analyses are expected associations with haematologic and oncologic traits, the associations we identify with obesity and body mass traits are of particular interest. This relationship between body mass and CHIP may relate to inflammatory or hormonal signalling, and directions of effect that we estimate are consistent with recent findings that DNMT3A CHIP reduces bone mineral density via increases in macrophage-mediated IL-20 signalling 39 . The fact that the association we report between obesity and body mass and CHIP are in opposing directions across CHIP subtypes (for example, negative in DNMT3A CHIP and positive in TET2 CHIP and ASXL1 CHIP) suggests that the relationship between CHIP and adiposity is complex and requires further investigation. Perhaps most unexpectedly, we found associations between CHIP and CVD to be more modest than previously reported 1 , 2 , 3 . DNMT3A mutations do not associate with CVD, which is consistent with the absence of any association between CHIP and CVD when applying Mendelian randomization. However, this pattern is not seen across CHIP associations with solid tumours, which we found to be driven by DNMT3A , and to be supported by Mendelian randomization. Overall, our results further clarify the role of CHIP mutational subtypes in the development of cancer and CVD and emphasize the importance of viewing (and potentially treating) different CHIP subtypes as distinct haematologic preconditions. Whereas Bick et al. 6 . found statistical support for reduced CVD incidence among CHIP carriers with an IL6R coding mutation (rs2228145-C) serving as a genetic proxy for IL-6 inhibition, we do not find any support for this association when extending their analysis from the first 50,000 exomes in the UKB to the full cohort of 450,000 exomes, nor when repeating this analysis in 175,000 exomes from the GHS cohort. The signal identified across the first 50,000 exomes may result from a chance ascertainment bias 40 . Alternatively, whereas the rs2228145-C variant is thought to mimic IL-6 inhibition, and therefore confer protection from heart disease 41 , neither our analysis nor Bick et al. found evidence that rs2228145 carriers are protected from CVD in subjects without CHIP. Therefore, it is possible that this mutation is a poor proxy for IL-6 inhibition, and that direct pharmacological inhibition of IL-6 may still antagonize the interplay between CHIP clone expansion and the onset of CVD. This study benefits from its biobank-scale size, which we leverage to further resolve clonal haematopoiesis subtypes and broadly assess clinical phenotypes associated with CHIP. However, limitations include the potential inclusion in our CHIP callset of a small number of germline variants, a lack of serial sampling, and a lack of experimental data to characterize the mechanisms underpinning the novel associations that we identify. Although we have taken many steps to ensure the quality of our callset and analysis (Supplementary Notes 11 and 12 and Supplementary Figs. 15 – 18 ), the misclassification of somatic variants with high VAF as germline variants, and/or the misclassification of true germline variants as somatic clonal haematopoiesis variants (for example, germline variants at genomic positions identified as clonal haematopoiesis hotspots) remain challenges inherent to calling and analysing CHIP and clonal haematopoiesis when using population scale genomic data. Serial sampling would enable the evaluation of changes to CHIP clones over time, and future studies that focus on such serial analysis at large scale will be able to better estimate CHIP subtype-specific clonal changes and clinical risk. Such increased data assets would also likely facilitate the identification of additional genes that show recurrent mutation during clonal haematopoiesis, as well as how such mutations relate to one another (that is, in dependency, mutual exclusivity and temporal order). Nonetheless, we identify many novel common and rare variant associations with CHIP and other clonal haematopoiesis phenotypes, which help to set the stage for future functional, mechanistic and therapeutic studies. On the whole, our analyses emphasize that CHIP is really a composite of somatic mutation-driven subtypes, with shared genetic aetiology and distinct risk profiles. Methods Study approval UKB study: ethical approval for the UKB study was previously obtained from the North West Centre for Research Ethics Committee (11/NW/0382). The work described herein was approved by UKB under application number 26041. GHS study: approval for DiscovEHR analyses was provided by the Geisinger Health System Institutional Review Board under project number 2006-0258. Exome sequencing and variant calling Sample preparation and sequencing were done at the Regeneron Genetics Center as previously described 10 , 40 . In brief, sequencing libraries were prepared using genomic DNA samples from the UKB, followed by multiplexed exome capture and sequencing. Sequencing was performed on the Illumina NovaSeq 6000 platform using S2 (first 50,000 samples) or S4 (all other samples) flow cells. Read mapping, variant calling and quality control were done according to the Seal Point Balinese (SPB) protocol 40 , which included the mapping of reads to the hg38 reference genome with BWA MEM, the identification of small variants with WeCall, and the use of GLnexus to aggregate these files into joint-genotyped, multi-sample VCF files. While certain UKB exome analysis efforts have used calls generated with the OQFE pipeline 42 , this pipeline has only been used to a limited degree for disease association analysis. Therefore, we chose to use calls from the SBP pipeline, which have been used very extensively for disease association analysis, including the largest set of association analyses done with UKB exome data 10 . Depth and allelic valance filters were then applied, and samples were filtered out if they showed disagreement between genetically determined and reported sex, high rates of heterozygosity or contamination (estimated with the VerifyBamId tool as a FREEMIX score > 5%), low sequence coverage, or genetically determined sample duplication. Calling CHIP To call CHIP carrier status, we first used the Mutect2 (GATK v4.1.4.0) somatic caller 43 to generate a raw callset of somatic mutations across all individuals. This software aims to use mapping quality measures as well as allele frequency information to identify somatic mutations against a background of germline mutations and sequencing errors. We used data generated from gnomAD v2 as the reference source for germline allele frequency 44 . We generated a cohort-specific panel of normals, which Mutect2 uses to estimate per-site beta distribution parameters for use in refining somatic likelihood assignment. Since CHIP is strongly associated with age, we chose 100 random UKB samples from 40 year olds and 622 samples from individuals less than 18 years of age in GHS to build these cohort-specific panels of normals. By evaluating the degree to which default Mutect2 filtering excluded known CHIP hotspot mutations, we noted that the default Mutect2 pass/fail filters were too stringent. Therefore, we initially considered all Mutect2 variants (that is, even those that did not pass default Mutect2 filtering), and proceeded to perform our own QC and somatic mutation call refinement. As an initial refinement step, we selected variants occurring within genes that have been recurrently associated with CHIP according to recent reports from the Broad 2 , the TOPMed Consortium 11 , and the Integrative Cancer Genomics (IntOGen) project 45 . We then filtered putative somatic mutations using the outlined functional criteria 2 . Next, we performed additional QC steps, which consisted of (1) removing multi-allelic somatic calls, (2) applying sequencing depth filters (total depth (DP) ≥ 20; alternate allele depth (AD) ≥ 3, F1R2 and F2R1 read pair depth ≥ 1), (3) removing sites flagged as panel of normals by Mutect2 (unless previously reported), (4) removing indels flagged by the Mutect2 position filter, (5) removing sites within homopolymer runs (a sequence of ≥5 identical bases) if AD < 10 or VAF < 0.08, (6), removing missense mutations in CBL or TET2 inconsistent with somaticism (that is, P -value > 0.001 in a binomial test of VAF = 0.5), (7) removing novel (not previously reported) variants that exhibited characteristics consistent with germline variants or sequencing errors. That is, we excluded variants that had a median VAF ≥ 0.35, since approximately 97% of previously reported variants (that is, from a recent study of CHIP by the TOPMed consortium 11 ) had a median VAF < 0.35. Beyond this, we evaluated the frequency distributions of known variants (stratified by effect—that is, missense or non-missense) to discern thresholds for newly identified variants (that is, AF (allele frequency) of novel variants ≤ AF of previously reported variants). Additionally, novel G>T or C>A SNV calls were evaluated for oxidation artifacts 46 . Specifically, variants with a maximum alternate allelic depth < 6 (across all samples) and < 2 supportive reads from F1R2 (C>A) or F2R1 (G>T) mate pairs were removed, respectively. Given that > 90% of mutations belonged to 23 recurrent CHIP-associated genes, we restricted to variants occurring within these genes as a final step to maximize the specificity of our callset. These genes consisted of the 8 most frequent mutated CHIP genes ( DNMT3A , TET2 , ASXL1 , PPM1D , TP53 , JAK2 , SRSF2 and SF3B1 ), a collection of CHIP-associated genes containing SNV hotspots ( BRAF , CSF3R , ETNK1 , GNAS , KRAS , GNB1 , IDH2 , MPL , NRAS , PHF6 and PRPF8 ), and CHIP-associated genes of haematological interest ( CBL , CALR , RUNX1 and SUZ12 ). Our final CHIP set of CHIP mutation carriers consisted of 29,669 CHIP mutations across 27,331 unique individuals from UKB, and 14,766 CHIP mutations across 12,877 unique individuals from GHS. Variant allele fraction (VAF) was calculated using AD/(reference allele depth (RD) + AD). Defining CHIP and mosaic phenotypes CHIP phenotypes were derived based on our mutation callset, whereas mosaic chromosomal alteration (mCA) phenotypes were derived based on previously published mCA calls from the UKB 4 , 7 , 8 . First, we used International Classification of Diseases (ICD) codes to exclude 3,596 samples from UKB and 1,222 samples from GHS that had a diagnosis of blood cancer prior to sample collection. We also excluded 13,004 individuals from GHS whose DNA samples were collected from saliva as opposed to blood. For all of the phenotypes we generated and analysed in this study, we used a combination of cancer registry data, hospital inpatient (HESIN) data, and data from general practitioner records to ascertain ICD10 codes. The majority of our cancer data came from the cancer registry, which we supplemented with the other sources. We then defined multiple CHIP and mosaic phenotypes based on whether carriers did (inclusive) or did not (exclusive) have other somatic phenotypes. For example, individuals with at least one CHIP mutation in our callset were defined as carriers for a CHIP_inclusive phenotype, whereas anyone with a CHIP mutation as well as an identified mCA was removed from this inclusive phenotype in order to define a CHIP_exclusive phenotype (20,606 cases and 342,869 controls). Our association analysis with CHIP used this CHIP_inclusive phenotype, which included 25,657 cases and 342,869 controls of European ancestry in UKB, and 11,821 cases and 135,106 controls of European ancestry in GHS. These counts reflect the samples with European ancestral origin that remain in each cohort after removing those with non-CHIP clonal haematopoiesis (60,991 in UKB and 0 in GHS, as we did not call mosaic chromosomal alterations in GHS), and those with missing meta data (348 in UKB and 4,893 in GHS). We defined mLOY carriers as male individuals with a Y chromosome mCA in the UKB mCA callset that had copy change status of loss or unknown, mLOX as individuals with an X chromosome mCA in the UKB mCA callset that had copy change status of loss or unknown, and mCAaut carriers as individuals with autosomal mCAs. We then refined these inclusive phenotypes to define exclusive versions, with mLOY_exclusive consisting of carriers with no X chromosome or autosomal mCAs (36,187 cases and 151,161 controls), mLOX_exclusive consisting of carriers with no Y chromosome or autosomal mCAs (10,743 cases and 364,072 controls), and mCAaut_exclusive consisting of carriers with no Y or X chromosomal alterations of any kind (11,154 cases and 364,072 controls). These exclusive phenotypes were used for all analyses comparing CHIP with mosaic phenotypes, as this approach facilitated the generation of four non-overlapping phenotypes (that is, CHIP, mLOY, mLOX, and mCAaut) that could be compared. We also defined CHIP gene-specific phenotypes by choosing carriers as those with mutations in our callset from a specific gene and no mutations in any other of the 23 CHIP genes defining our callset. For example, CHIP DNMT3A carriers were those with ≥ 1 somatic mutations in our callset within the DNMT3A gene, and no mutations in our callset in any of the other 23 CHIP genes we used for our final callset definition. The set of 364,072 controls used in UKB that had no evidence of any clonal haematopoiesis (that is, no CHIP or mCAs) was considered as our set of healthy controls, and was used across all association analyses in UKB. Genetic association analyses To perform genetic association analyses, we used the genome-wide regression approach implemented in REGENIE 47 , as described 10 . In brief, regressions were run separately for data derived from exome sequencing as well as data derived from genetic imputation using TOPMed 48 , and results were combined across these data sources for downstream analysis. Step 1 of REGENIE uses genetic data to predict individual values for the trait of interest (that is, a polygenic risk score), which is then used as a covariate in step 2 to adjust for population structure and other potential confounding. For step 1, we used variants from array data with a MAF > 1%, < 10% missingness, Hardy–Weinberg equilibrium test P -value > 10 −15 and LD pruning (1,000 variant windows, 100 variant sliding windows and r 2 < 0.9), and excluded any variants with high inter-chromosomal LD, in the major histocompatibility region, or in regions of low complexity. For association analyses in step 2 of REGENIE, we used age, age 2 , sex and age × sex, and 10 ancestry-informative principal components as covariates. For analyses involving exome data, we also included as covariates an indicator variable representing exome sequencing batch, and 20 principal components derived from the analysis of rare exomic variants (MAF between 2.6 × 10 −5 and 0.01). Significance cutoffs and rare variant burden testing were set according to the power calculations and logic outlined by Backman et al. 10 . In brief, we used P ≤ 5 × 10 −8 , P ≤ 7.14 × 10 −10 , P ≤ 3.6 × 10 −7 , for common, rare and burden associations, respectively. Results were visualized and processed using an in-house version of the FUMA software 49 . Association analyses were performed separately for different continental ancestries defined based on the array data, as described 10 . Replication of associations signals in the GHS cohort To calculate the power to achieve replication in the GHS cohort, we first adjusted for the effects of ‘winner’s curse’, which are expected when choosing significant associations signals on the basis of a genome-wide threshold 50 . To do this, we used the conditional likelihood approach described by Ghosh et al. 51 as implemented in the winnerscurse R package (version 0.1.1), which adjusts the estimated betas from genome-wide significant associations signals. These adjusted effect estimates are provided in Supplementary Table 2 (column Effect_adj). We then used these adjusted effect estimates to calculate the expected power to detect each lead signal in the GHS replication phase using the GHS sample size, allele frequencies, CHIP prevalence, and an alpha level of 0.05. To summarize our expected power across the replication phase, we summed the power across all lead variants and reported the number of SNPs that replicated at P < 0.05 as a proportion of the cumulative power to detect those variants. Identifying independent signals from association results We used three different approaches to identify independent signals across loci that associated with CHIP. First, we used a clumping and thresholding approach (C&T) 52 in which index SNPs at each significantly associated locus were defined greedily as those with the lowest P -value. Clumping was then done by extending linkage blocks laterally to include all SNPs that have P < 1 × 10 −5 and r 2 > 0.1 with the index SNP. Any SNP within a clump was then removed from further analysis. This process was repeated as long as there was ≥ 1 additional SNP in the locus with P ≤ 5 × 10 −8 . After all clumps were made, we merged any clumps (that is, LD blocks) with overlapping genomic ranges. Since this approach did not feature any iterative conditioning nor model variant effects jointly, we also used conditional joint analysis as implemented in GCTA COJO 53 and statistical fine-mapping as implemented in FINEMAP 54 to identify independent/causal signals. COJO was run with a subset of 10,000 unrelated European ancestry samples from UKB as an LD references, and with a COJO adjusted P -value threshold of 5 × 10 −6 , an info score threshold of 0.3, and a MAF cutoff of 0.01. FINEMAP was run with the shotgun stochastic search algorithm using a maximum of 30 causal variants. We included variants in the FINEMAP analysis that had P < 0.1 in inverse variance weighted meta-analysis, and MAF > 0.001. The LD matrices used for the FINEMAP analysis were constructed as weighted meta LD matrices derived from the LD matrices from UKB and GHS. The LD matrices from UKB and GHS were computed independently using the same sets of samples included in each GWAS. Fine-mapping variants at the LY75 locus To further evaluate whether the rare variant association at the LY75 locus (rs147820690-T) was independent of other common and rare variant signals, we performed joint fine-mapping (with FINEMAP) on common and rare variants at this locus while including rarer variants then used in our genome-wide fine-mapping. In contrast to the genome-wide fine-mapping described above, this fine-mapping sensitivity analysis was done only in the UKB, was focused on the LY75 locus, and included all variants in our dataset. That is, the fine-mapping analysis was run as described above, but with a MAF > 0.0000000001. While FINEMAP suggests 3 credible sets are most parsimonious at this locus (posterior probability = 0.8), which is consistent with the results we report when preforming genome-wide fine-mapping, the fourth credible set (posterior probability = 0.11) identifies rs147820690-T as the top signal (PIP = 0.133) among 9,417 variants in the 95% credible set. This fine-mapping approach also prioritizes rs78446341-A (CPIP = 0.92, CS = 2). Furthermore, the median pairwise LD between SNPs in this fourth credible set is very low (6.7 × 10 −4 , compared with 0.995, 0.962, and 0.831 for the first three credible sets, respectively). Therefore, these fine-mapping results provide additional support for both LY75 missense variants, as well as the fact that the rs147820690-T rare variant signal is not driven by the tagging of other rare variants. PheWAS across CHIP-associated variants Using 937 traits from the UKB, we queried association results for 171 SNPs from our GWAS of CHIP. These SNPs represent the union of those identified by clumping and thresholding, conditional analysis with GCTA COJO, and fine-mapping with FINEMAP (fine-mapped SNPs were chosen if they had one of the highest two posterior inclusion probabilities—that is, PIPs—in any credible set). While this group of SNPs does include signals with P < 5 × 10 −8 in our CHIP GWAS, these SNPs represent signals prioritized as conditionally independent and/or likely to be causal, and we therefore deemed them worthy of exploration via PheWAS. Some of these subthreshold signals featured many significant PheWAS associations ( P < 5 × 10 −8 in the PheWAS), and likely merit further evaluation (for example, ZFP36L2 / THADA locus on chromosome 2, and THRB locus on chromosome 3). The traits used in this PheWAS represent the subset of the 5,041 traits used in our cross-sectional analyses of phenotypic association with CHIP mutations carrier status for which we have previously reported common variant associations 10 . In brief, for ICD10-based phenotypes, cases were required to have one or more records of diagnosis in the electronic health records, death registry data implicating the disease, or two or more diagnosis in outpatient data mapped to ICD10. For non-ICD10 phenotypes (quantitative measures, clinical outcomes, survey and touchscreen responses, and imaging derived phenotypes), data were derived from the UKB Showcase. Participants who did not meet the case definition for a given ICD10-based phenotype were removed from the analysis if they had one diagnosis code in the outpatient data, and included as controls if they had no diagnosis in the outpatient data. Supplementary Table 10 includes ICD10 codes as well as trait names and descriptions. Genetic comparisons between CHIP subtypes For pairwise comparisons between CHIP gene mutation subtypes, we used the union set of index SNPs (that is, independent signals in genome-wide significant loci) from all of our CHIP and CHIP gene subtype associations. This resulted in 93 variants, which we used to compare effect sizes estimates between CHIP subtype pairs. Genetic correlations were calculated using LDSC version 1.0.1 with annotation input version 2.2 22 . Defining smoking phenotypes We derived smoking phenotypes from the lifestyle and environment questionnaire in the UKB and from the electronic health records in the GHS. Since smoking is difficult to ascertain and control for, we used a variety of data to code multiple smoking phenotypes for various analyses. These smoking phenotypes consisted of (1) pack years, (2) number of cigarettes smoked per day, (3) age started/stopped smoking (UKB only), (4) former/current smoker, (5) ever smoker and (6) heavy smoker (smoked ≥ 10 cigarettes a day). The ever smoker phenotype was maximally inclusive, and coded as cases all individuals with any evidence of prior smoking across the aforementioned phenotypes. For our longitudinal analyses in UKB, we used the ‘current smoker’ and ‘pack years’ (which captures the cumulative effect of smoking over one’s lifetime) as covariates in all models that did not stratify for smoking status. In the smoking stratified models, we stratified smokers based on the ‘ever smoker’ phenotype and further adjusted for pack years within the smokers subgroup. For our longitudinal analyses in GHS, we used the ‘ever smoker’ and ‘pack years’ phenotypes as covariates in all models that did not stratify for smoking status, and stratified smokers in the same manner as we did in the UKB analyses. For linear models that evaluated the overall relationship between age, sex, and smoking, we used the ‘heavy smoker’ coding. Otherwise, all other analyses used the aforementioned ‘ever smoker’ phenotype as a covariate. Phenotypic associations with CHIP To test for known as well as potentially novel associations, we used REGENIE 47 to perform Firth-corrected tests for association between our CHIP gene-specific phenotypes and 5,041 traits (2,640 binary traits and 2,401 quantitative traits) from the UKB (version 5). To do this, we coded each CHIP gene-specific phenotype as 1 if an individual had any somatic CHIP mutation in the gene and 0 otherwise and formatted these binary codings as pseudo-genotypes to analyse with REGENIE. Regression models were run as described previously, with age, sex, and genetic principal components as covariates 10 . After filtering out association tests where the total number of somatic carriers was <5, we were left with 83,779 total association tests (Supplementary Table 31 ). Only 22 out of 23 CHIP gene subtypes were tested for association across phenotypes as we did not have enough carriers of CSF3R mutations to meet our minimum threshold of 5 somatic carriers that were also disease cases. Quantitative traits were transformed using a reverse inverse normalized transformation (RINT); effect size estimates from these associations are in units of standard deviation. Traits used in this analysis did not exclude any samples on the basis of having a diagnosed haematological disease or malignancy prior to sequencing date. To visualize high-level phenotypic patterns across these CHIP gene-specific phenotypes (Fig. 3 ), we categorized phenotypes by disease group 10 , and calculated the proportion of phenotypes per disease group per gene that were associated at a P ≤ 0.05 alpha level (uncorrected). To visualize the most significant of these associations, we plotted effect sizes (Supplementary Fig. 7 ) by disease category for all associations with P ≤ 1 × 10 −5 . Risk modelling among CHIP carriers We performed longitudinal survival analyses using cox proportional hazard models (coxph function) as implemented in the survival R package. Given that CHIP is strongly correlated with age, models used age as the time scale with interval censoring with age at first assessment and age at event or censoring. This allows for an implicit adjustment for age within the proportional hazard models. In UKB, individuals with follow-up time in excess of 13.5 years (3% of the dataset) were censored due to departures from the proportional hazards model. Analyses were performed on individuals of European ancestral background. All models included 10 genetically determined European-specific principal components as covariates, and all analyses excluded individuals genetically determined to be third-degree relatives or closer. In GHS, we had limited sample size with which to perform these longitudinal analyses. This was because GHS samples were collected at later ages (due to the nature of the biobank and the timing of our partnership) and fewer patients had disease onset dates subsequent to sample collection (that is, the time period where the onset of CHIP can be evaluated). Furthermore, in GHS, we could not derive an all-cause mortality phenotype due to the nature of the EHR data available to us. This incomplete ascertainment may also explain why our odds ratio estimates for risk of haematologic malignancy among CHIP carriers are lower in the GHS cohort. We used a variety of CHIP codings as variables in our models to test for potential differences between high/low VAF CHIP and/or CHIP subtypes. First, we subset CHIP carrier status by gene ( DNMT3A , TET2 , ASXL1 , DNMT3A or TET2) and/or VAF (≥0.1) to test for potential differences between degree of clonal expansion (that is, high/low VAF CHIP) and/or CHIP subtypes. Additional analyses were run restricting CHIP mutation calls to previously reported variants (for example, Jaiswal et al. 2 ), as well as restricting to carriers of DNMT3A mutations with at least one mutation in another CHIP gene. Controls were defined with two approaches: (1) any individual without CHIP mutations (the coding used in the results we report) and (2) those without any genetic evidence of clonal haematopoiesis (that is, healthy controls, as defined above, which did not change our results). The CHIP gene-specific coding described above varies from the phenotypic coding definitions used in our GWAS/ExWAS, which required carriers to have mutations only in the specified CHIP gene and no mutations in any other CHIP genes. Since mutational exclusivity becomes less common as VAF increases (that is, carrying a single mutation with VAF ≥ 0.1 and no other mutations), and substantially lowers sample size, we chose this adjusted definition for these longitudinal analyses of disease incidence. For the composite phenotypes described below, we relied heavily on ICD10 codes from cancer registry data, hospital records and general practitioner records, and supplemented these with self-reported data and procedure codes (OPCS4). We defined prevalent disease on the basis of event codes occurring before sample collection and used this definition to exclude samples from longitudinal analysis of incident disease. For these main analyses, we did not use any minimum number of days to diagnosis from sample collection as an additional filtering criterion (see Supplementary Note 12 for more details). In UKB, cardiovascular disease was defined with the following ICD10 codes obtained from primary care, HES (hospital episode statistics), or death registry data: I21, I22, I23, I252, I256, Z951, Z955, I248, I249, I241, I251, I255, I258, I259, I630, I631, I632, I633, I634, I635, I637, I638, I639, I651, ICD9 codes: 410, 412, and OPCS codes: K40, K41, K44, K45, K46, K49, K502, K75 and K471. ICD9/ICD10/OPCS diagnoses or procedures recorded prior to enrolment date and self-report codes 1075 (heart attack/myocardial infarction), 1095 (cabg), 1523 (heart bypass), 1070 (coronary angioplasty or stent), 1583 (ischaemic stroke), 1083 (stroke) were used to identify prevalent CVD cases. These were chosen to best reflect the coding use by Bick et al. in their study of CHIP 6 . In GHS, we used ICD10 codes I20–I25 and I60–I69, CPT codes from 33510–33523 (CABG, not continuous), 33533–33536, 35500, 35572, 35600, and 92920–92975 (PCI, not continuous). We also adjusted the CVD coding in GHS to exclude cerebrovascular events (that is, excluded I60–I69); association results were similar. The CVD coding we used for our Mendelian randomization analysis was comparable to these definitions but did not include ICD10 codes for cerebrovascular events. For the CVD models, we included sex, LDL, HDL, pack years, smoking status (current vs former, determined by self-reported data), BMI, essential primary hypertension, and type 2 diabetes mellitus as covariates. The results we reported used a composite of myocardial infarction (MI), coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), and coronary artery disease (CAD), based on the coding described above, and also included death from any of these events. Results were similar when our composite included ischaemic stroke (ISCH.TR), as well as when we repeated analyses with a subset of recurrent CHIP mutations derived from Jaiswal et al. 2 or restricting carrier calls to variants in DNMT3A or TET2 . We also excluded samples with any diagnosis of malignant blood cancer prior to sequencing ( n = 3,596). Missing LDL and HDL values were median imputed, and individuals on cholesterol medication had their raw LDL values increased by a factor of 1/0.68, similar to Bick et al. 6 . IL6R missense variant (rs2228145-C) genotypes were modelled dominantly (coded as 1 for carriers of any allele and 0 otherwise), and we modelled the effect of this allele in CHIP -stratified proportional hazard models, and also tested for IL6R × CHIP interaction in a full (non-stratified) model. Models considering only the initial 50k UKB individuals restricted to intersection between our unrelated UKB sample set and the samples reported by Bick et al. 6 . For visualization, Kaplan–Meier estimates were generated with the survfit function in the aforementioned survival package (version 3.2.13) and plotted using the ggsurvplot function from the survminer package (version 0.4.9). For models of cancers and overall survival risk tested using all CHIP carriers, high-VAF (VAF ≥ 0.1) CHIP carriers, and carriers of specific CHIP gene mutations, we used unrelated European samples that did not have any cancer diagnoses prior to sample collection (N = 360, 051 after the removal of 33,816 samples with a prior diagnosis of cancer). Results were qualitatively the same when repeating these analyses without excluding samples that had a diagnosis of any malignant cancer prior to sample collection date. Cancer phenotype definitions were derived from medical records indicating the following ICD10 codes: C81–C96, D46, D47.1, D47.3, D47.4 for blood cancers, C81–C86, C91 for lymphoid cancers, C92, C94.4, C94.6, D45, D46, D47.1, D47.3, D47.4 for myeloid cancers, C50 for breast cancers, C34 for lung cancers, C61 for prostate cancers, C44 for non-melanoma skin cancers (NMSC), and C18 for colon cancers (five total solid cancers). Myeloid subtypes were defined as follow: AML (C92), MDS (D46), MPN (D47.1, D47.3, D47.4). Given the rareness and/or non-specificity of myeloid codings C93–95, and that the majority of these codings overlapped with those that we used for the myeloid composite described above (that is, we already captured these samples using the previously described codings), we did not include these codings in our composite. However, we performed sensitivity analyses that used a myeloid definition that did include C93–C95, with findings equivalent to those described in our main results (Supplementary Note 12 ). For our lymphoid composite, we decided to combine lymphoma with lymphoid leukaemia for multiple reasons. First, in some clinical diagnostic situations (for example, T cell lymphoblastic lymphoma and T cell lymphoblastic leukaemia; Burkitt lymphoma and mature B cell ALL), the distinction between ‘leukaemia’ and ‘lymphoma’ is made on the basis of blast percentage in bone marrow (that is, > 20% blasts diagnosed as leukaemia), and may not reflect meaningful biological differences. Consistently, 22% of C91 codings are already captured in our C81–C86 codings. Moreover, the majority of cases across these codings correspond to tumours derived from mature B cells, namely chronic lymphocytic leukaemia (CLL) and mature non-Hodgkin lymphoma. Given data supporting that mature T cell lymphomas and also some mature non-Hodgkin B cell tumours may arise from hematopoietic stem and progenitor cells 55 , 56 , 57 , we considered the relationship between a composite of mature lymphoid tumours and CHIP. For blood cancers, we also included cases that self-reported leukaemia, lymphoma, or multiple myeloma. These models included the same covariates as described for CVD (with the exception that we did not adjust cholesterol level based on medication usage). Additionally, models estimating risk for sex-specific cancers (that is, prostate and breast) restricted to individuals of the relevant sex and did not adjust for sex as a covariate. For smoking stratified modelling of blood and lung cancer, we used our stricter definition of smoking (ever vs never) and included pack years as a covariate in models testing risk among smokers. To test a more conservative cutoff for excluding patients with a diagnosis of haematologic malignancy prior to sequencing (that is, exclude individuals with a diagnosis prior to 90 days after DNA collection date rather than prior to the DNA collection date itself), we conducted sensitivity analyses for the longitudinal modelling of the risk among CHIP carriers of acquiring blood cancers (for example, blood cancer, myeloid, lymphoid, AML, MDS and MPN). These results were the same as those reported in our main results (Supplementary Note 12 ). Polygenic risk scores Polygenic risk scores were calculated with Plink 58 as a weighted sum of the effects across all conditionally independent variants we identified with GCTA COJO (74 variants, P ≤ 5 × 10 −6 ) We performed association tests using logistic regression, with binary phenotypes of interest (that is, our CHIP subtype phenotypes—for example, TET2 CHIP, and so on) as the dependent variable, this polygenic risk score as the independent variable of interest, and age, sex, smoking status (ever vs never), and 10 genetic principal components as covariates. Software The code is publicly available and can be found at . The REGENIE software for whole-genome regression, which was used to perform all genetic association analysis, is available at . GCTA v1.91.7 was used for approximate conditional analysis. SHAPEIT4.2.0 was used for phasing of SNP array data. Imputation was completed with IMPUTE5. Somatic calling was done with Mutect2 (GATK v4.1.4.0). We use Plink1.9/2.0 for genotypic analysis as well as for constructing polygenic risk scores. FINEMAP was used for fine-mapping, and genetic correlations were calculated using LDSC version 1.0.1 with annotation input version 2.2. Beyond standard R packages, visualization tools, and data processing libraries (for example, dplyr, ggplot2 and data.table), we used the survival (version 3.2.13) and survminer (version 0.4.9) packages for survival analyses, the MendelianRandomization package for Mendelian randomization (version 0.6.0), and the winnerscurse package (version 0.1.1; ) to adjust GWAS effect size estimates for the effects of Winner’s Curse. Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability Individual-level sequence data, CHIP calls and polygenic scores have been deposited with UK Biobank and are freely available to approved researchers, as done with other genetic datasets to date 10 . Individual-level phenotype data are already available to approved researchers for the surveys and health record datasets from which all our traits are derived. Instructions for access to UK Biobank data is available at . Summary statistics from UKB trait are available in the GWAS catalogue (accession IDs are listed in Supplementary Table 33 ). As described 10 , the HapMap3 reference panel was downloaded from ftp://ftp.ncbi.nlm.nih.gov/hapmap/ , GnomAD v3.1 VCFs were obtained from , and VCFs for TOPMED Freeze 8 were obtained from dbGaP as described in . Data used for replication, such as DiscovEHR exome sequencing and genotyping data, and derived CHIP calls, can be made available to qualified, academic, non-commercial researchers upon request via a Data Transfer Agreement with Geisinger Health System (contact person: Lance Adams, ljadams@geisinger.com). Change history 17 February 2023 A Correction to this paper has been published: | Medicine |
http://dx.doi.org/10.1038/s41598-020-72798-7 | https://medicalxpress.com/news/2020-09-surgical-n95-masks-block-particles.html | Surgical, N95 masks block most particles, homemade cloth masks release their own | "Laboratory tests of surgical and N95 masks by researchers at the University of California, Davis, s(...TRUNCATED) | "Sima Asadi et al. Efficacy of masks and face coverings in controlling outward aerosol particle emis(...TRUNCATED) | 10.1038/s41598-020-72798-7 | "Abstract The COVID-19 pandemic triggered a surge in demand for facemasks to protect against disease(...TRUNCATED) | Medicine |
http://dx.doi.org/10.1038/nnano.2015.106 | https://phys.org/news/2015-06-varying-properties-atoms-surface.html | Varying the sliding properties of atoms on a surface | "It's possible to vary (even dramatically) the sliding properties of atoms on a surface by changing (...TRUNCATED) | "\"Frictional transition from superlubric islands to pinned monolayers.\" Nature Nanotechnology (201(...TRUNCATED) | 10.1038/nnano.2015.106 | "Abstract The inertial sliding of physisorbed submonolayer islands on crystal surfaces contains unex(...TRUNCATED) | Nano |
http://dx.doi.org/10.1136/bmj.l6131 | https://medicalxpress.com/news/2019-11-trial-labor-approach-late-term.html | Trial suggests inducing labor over 'wait and see' approach for late term pregnancies | "Inducing labour at 41 weeks in low risk pregnancies is associated with a lower risk of newborn deat(...TRUNCATED) | "Induction of labour at 41 weeks versus expectant management until 42 weeks (Swedish post-term induc(...TRUNCATED) | 10.1136/bmj.l6131 | "Abstract Objective To evaluate if induction of labour at 41 weeks improves perinatal and maternal o(...TRUNCATED) | Medicine |
http://dx.doi.org/10.1038/s41467-020-15707-w | https://medicalxpress.com/news/2020-04-genome-sequencing-reveals-genetic-secrets.html | Whole genome sequencing reveals genetic structural secrets of schizophrenia | "Most research about the genetics of schizophrenia has sought to understand the role that genes play(...TRUNCATED) | "Matthew Halvorsen et al, Increased burden of ultra-rare structural variants localizing to boundarie(...TRUNCATED) | 10.1038/s41467-020-15707-w | "Abstract Despite considerable progress in schizophrenia genetics, most findings have been for large(...TRUNCATED) | Medicine |
http://dx.doi.org/10.1007/s11109-016-9332-1 | https://phys.org/news/2016-03-parties-rarely-presidential-elections.html | Divided parties rarely win presidential elections, study shows | "Divided political parties rarely win presidential elections, according to a study by political scie(...TRUNCATED) | "Paul-Henri Gurian et al. National Party Division and Divisive State Primaries in U.S. Presidential (...TRUNCATED) | 10.1007/s11109-016-9332-1 | "Abstract In presidential nomination campaigns, individual state primaries and a national competitio(...TRUNCATED) | Other |
http://dx.doi.org/10.1038/nature24472 | https://phys.org/news/2017-11-shadow-zone-world-oldest-ocean.html | How a 'shadow zone' traps the world's oldest ocean water | "New research from an international team has revealed why the oldest water in the ocean in the North(...TRUNCATED) | "C. de Lavergne et al, Abyssal ocean overturning shaped by seafloor distribution, Nature (2017). DOI(...TRUNCATED) | 10.1038/nature24472 | "Abstract The abyssal ocean is broadly characterized by northward flow of the densest waters and sou(...TRUNCATED) | Earth |
http://dx.doi.org/10.1038/s43247-021-00248-x | https://phys.org/news/2021-08-new-study-findings-could-help.html | New study findings could help improve flood projections | "Climate change will lead to more and stronger floods, mainly due to the increase of more intense he(...TRUNCATED) | "Manuela I. Brunner et al, An extremeness threshold determines the regional response of floods to ch(...TRUNCATED) | 10.1038/s43247-021-00248-x | "Abstract Precipitation extremes will increase in a warming climate, but the response of flood magni(...TRUNCATED) | Earth |
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SciNews
The SciNews dataset is designed to facilitate the development and evaluation of models that generate scientific news reports from scholarly articles. This dataset aims to bridge the gap between complex scientific research and the general public by simplifying and summarizing academic content into accessible narratives. It supports tasks like text summarization, simplification, and the automated generation of scientific news, providing a valuable resource for enhancing public engagement with science and technology.
Dataset Details
Dataset Sources
- Repository: The dataset and code related to this work are available at SciNews Project Page.
- Paper: The details about the dataset can be found in the paper "SciNews: From Scholarly Complexities to Public Narratives – A Dataset for Scientific News Report Generation" by Dongqi Pu, Yifan Wang, Jia Loy, Vera Demberg.
Dataset Creation
Data Collection and Processing
Data was collected from the Science X platform, an open-access hub for science, technology, and medical research news. Data extraction was performed using web scraping tools like Selenium and BeautifulSoup.
Annotations
The dataset does not include additional annotations as it is a compilation of existing scientific papers and their corresponding news reports. The quality control included automated and human assessments to ensure the relevance and quality of the news narratives in relation to the original scientific papers.
Recommendations
Users of the SciNews dataset should be aware of its limitations and biases, particularly when developing models for scientific news generation. Efforts should be made to address potential biases and ensure that generated narratives accurately and fairly represent the original scientific content.
Citation
BibTeX:
@inproceedings{pu2024scinews,
title={SciNews: From Scholarly Complexities to Public Narratives – A Dataset for Scientific News Report Generation},
author={Pu, Dongqi and Wang, Yifan and Loy, Jia and Demberg, Vera},
booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation.},
year={2024}
}
APA:
Pu, D., Wang, Y., Loy, J., & Demberg, V. (2024). SciNews: From Scholarly Complexities to Public Narratives – A Dataset for Scientific News Report Generation. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation.
Dataset Card Authors
This dataset card was created based on the paper by Dongqi Pu, Yifan Wang, Jia Loy, Vera Demberg from Saarland University, Germany.
Dataset Card Contact
For further inquiries or questions regarding the SciNews dataset, please contact the email address: dongqi.me@gmail.com
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Curated by:
Dongqi Pu, Yifan Wang, Jia Loy, Vera Demberg from the (1). Department of Computer Science and (2). Department of Language Science and Technology at Saarland Informatics Campus, Saarland University, Germany.
Funded by:
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 948878).
Language(s) (NLP):
English
Size of downloaded dataset files:
2.07 GB
Size of the auto-converted Parquet files:
1.03 GB
Number of rows:
41,872