We collected 78 gene panels published in literatures for the identification of neutrophils (Table S2). Among these panels, some of them were used to assess neutrophils in body fluids, such as peripheral blood, bone marrow, and bronchoalveolar lavage fluid. The other gene panels were used to evaluate neutrophil subsets in the tissue. Some of these gene panels were obtained from healthy or control samples, while some were from disease samples. Part of these gene sets were universal, meaning they can be used to evaluate both healthy samples and disease samples, or exist in both body fluid samples and tissue samples. The collected cell subsets were divided into several different hierarchies, some of which formed differentiation tracks among the cell subsets (Fig. 1a).

Distribution of neutrophil subsets in peripheral blood of six healthy donators. a Neutrophil subpopulations in hierarchy chart. Detected subgroups was marked in red. b Proportion of neutrophil subgroups of the healthy. c Specificity of detected neutrophil subsets

scRNA-seq data of peripheral blood cells from six healthy donors was obtained from published datasets. There were 32 neutrophil populations in all of the specimens, which were marked in red in Fig. 1a. Among them, Canonical_Neu occupied the largest proportion (Table S3), which was characterized by high expression of CSF3R, S100A8 and S100A9, indicating that this cell subpopulation performs essential functions of neutrophils, such as differentiation, cell adhesion, or recognition processes. In terms of proportion, it was closely followed by IL-7R + neutrophils, BALF_Neu and IFITM2_Neu. The specificity of 32 gene panels was evaluated according to OER referred above (Fig. 1c). As a result, ten specific panels and three associate panels were recognized (Tables 1, 2 and 3 and Table S4), illustrating that these 13 gene panels were relatively high reliable as clustering criteria in the healthy.

We evaluated the distribution of neutrophils by scRNA-seq in seven tumor tissues, six adjacent normal lung tissues, seven preoperative blood, and five postoperative blood samples from patients with NSCLC. Based on the previously established evaluation procedure, we found that several types of neutrophils were prevalent in the tissues and blood of lung cancer patients, namely Neu_c1_IL1B, Neu_c2_cxcr4(low), Neu_c3_CST7, IL-7R + neutrophils, Circulating neutrophils, Naïve neutrophils, IFN_experienced neutrophils, mNeu_14_ Lgals1, S100A12/Pabpc1_Neu, TXNIP/Gm2a_Neu, CD74_Neu and several general neutrophil groups (Fig. 2a-c, Table S5), whereas CD34_mNeu was expressed specifically in NSCLC. Figure 2d exhibited statistical overview of co-expressed subpopulations of neutrophils. Among these populations, there were significant differences in expression of Classical_Neu and TXNIP/Gm2a_Neu between preoperative blood and NSCLC, while the proportion of Neu_c5_GSTP1(high)OASL(low), IL-7R + neutrophils, and IFIT1_Neu was significantly different between preoperative blood and para-carcinoma tissue (Fig. 2e). In addition, there were nine subpopulations whose proportion was significantly different between preoperative blood and both the tissues (Fig. 2f). The distribution of several subsets was also comparable between postoperative blood and tissues, showed in Fig. 2g.

Proportion and specificity of neutrophil subsets in NSCLC. a Detected neutrophil subpopulations of NSCLC and para-carcinoma tissue in hierarchy chart, which were marked in red and green, respectively. b Detected neutrophil subpopulations of preoperative and post operative blood in hierarchy chart, which were marked in yellow and purple, respectively. c Proportion of neutrophil subgroups. d Co-expressed subgroups in NSCLC. The number of shared cell groups was displayed on the Y axis. e–g Neutrophil subpopulations with significant differences in the expression of NSCLC, para-carcinoma tissue, preoperative and postoperative blood. h Specificity of neutrophil subgroups in NSCLC. Specific panel, associate panel and reference panel were exhibited as blue, pink, and red, respectively

The distribution specificity of neutrophil subsets was assessed according to the criteria mentioned above (Fig. 2h, Table S6-S9). Although Neu_c1_IL1B and Neu_c2_cxcr4(low) are widely exist in lung cancer, para-carcinoma tissue, preoperative and postoperative blood from NSCLC patients, the specificity of their distribution varied significantly. In peripheral blood, especially postoperative blood, the increased specificity of these two populations suggests that they are more credible as prognostic biomarkers of circulatory system (OER10 = 96.3%). Proportion of IL-7R + neutrophils in peripheral blood also may be a useful guideline (OER10 = 96.43% in preoperative blood, OER10 = 88.89% in postoperative blood). It is worth noting that some gene panels were specific or associated in all the four NSCLC-associated samples (Neu_c3_CST7, RSAD2_Neu, S100A2/Pabpc1_Neu, ISG15/Ifit3_Neu, CD74_Neu, PTGS2/Actg1_Neu, SPP1_Neu), meaning that changes in the distribution of these cell populations would have a high degree of confidence in assessing disease changes. The specificity changes between cancer and para-carcinoma tissues or between preoperative and postoperative blood may be due to differentiation or migration of cell subsets in response to changes in the disease microenvironment.

Then, we obtained the single cell sequencing results of different lung diseases from the GEO database, and also evaluated distribution of the neutrophil populations (Fig. 3a-g, Table S10). A total of 18 subgroups were prevalent in all lung tissue samples. S100A12/Pabpc1_Neu, ELL2/Marco_Neu, SPP1_Neu and PTGS2/Actg1_Neu existed in PC_NOR, COPD, SCC and LUAD. Tnf_Neu was the common cell population of IPF, NOR, SCC, LUAD, and COPD. CSF1_Neu was expressed in both IPF and NOR. NOR specifically expressed Neu_c3_CST7 (Fig. 3h). All of the above information provides a reference for the diagnosis of subtypes of different lung diseases.

Proportion and specificity of neutrophil subsets in lung diseases, including 49 NOR, 7 PC_NOR, 18 COPD patients, 40 IPF patients, eight SSC patients, and 24 LUAD patients. a–f Detected neutrophil subpopulations of lung diseases in hierarchy chart. g Proportion of neutrophil subgroups. h Co-expressed subgroups among different lung diseases. The number of shared cell groups was displayed on the Y axis. i Specificity of neutrophil subgroups among lung diseases. Specific panel, associate panel, and reference panel were exhibited as blue, pink, and red, respectively

Several groups of cells were nonspecifically expressed in each sample, such as Neu_c2_CXCR4(low), Neu_c5_GSTP1(high)OASL(low), IL-7R + neutrophils, Naïve neutrophils, Canonical_Neu, IFITM2_Neu,Anxal_Neu, Lgals1_mNeu. In the contrast, Tnf_Neu, Rps19_Neu and Cd34_mNeu showed high specific expression in various lung diseases and normal tissues (Fig. 3i, Table S11-S16). However, although some cell populations are also commonly distributed in various lung disease samples, their specificities are significantly different. For example, Circulating neutrophils was significantly more specific in LUAD, NOR, and PC_NOR, especially in PC_NOR (OER10 = 95.65%). Isg15_Neu and Psap_Neu were decided as reference panels in LUAD, but specificity has been increased in other samples, especially Psap_Neu was specific panel in COPD, IPF and NOR (OER10 = 62.5%, 90.48% and 86.36%, respectively). S100A12/Pabpc1_Neu and PTGS2/Pabpc1_Neu was not detected in IPF and NOR, but specificity was significantly specific in COPD, LUAD, PC_NOR and SCC.

The distribution of neutrophil subsets different diseases has been evaluated (Fig. 4a, Table S17). In general, the expression of cell subsets in various organs was extremely different. CD74_Neu, Lgals1_mNeu, and IL-7R + neutrals were the most common subgroups. Among them, IL-7R + neutrophils was a superior cell group in Alzheimer’s disease, GATA2 deficiency with susceptibility to MDSAML, breast ductal adenocarcinoma, diffuse gastric adenocarcinoma, and retinoblastoma. The proportion of Lgals1_mNeu in some diseases was absolutely dominant, such as multiple sclerosis, COVID-19, amyotrophic lateral sclerosis, asthma, enamel caries, end stage renal failure, familial hypercholesterolemia, and myocardial infarction. CD74_Neu was widely present in PBMCs of multiple sclerosis, autoimmune lymphoproliferative syndrome cirrhotic, crohn ileitis, and chronic periodontitis. In addition, there were a large number of Cd34_mNeu in atypical chronic myeloid leukemia. There was a high proportion of IFNactive neutrophils, IFN_experienced neutrophils, Isg15_Neu, and ISG15/Ifit3_Neu in chronic rhinosinusitis. In blastoma, APOA2 _Neu occupied the majority. Comparatively, a larger variety of neutrophil subsets existed in cirrhotic, clear cell renal carcinoma, colorectal cancer, down syndrome, chronic periodontitis, and chronic rhinosinusitis, which indicated a higher heterogeneity of the immune environment.

Proportion and specificity of neutrophil subsets in 36 kind of diseases. a Proportion of neutrophil subgroups. b Specificity of neutrophil subgroups among various diseases. Specific panel, associate panel, and reference panel were exhibited as blue, pink, and red, respectively

Then we have compared the distribution of neutrophil subsets from different diseases of the same tissue to evaluate its feasibility in the identification of disease subtypes. Compared with atypical chronic myeloid leukemia and GATA2 deficiency, which are both from bone marrow, atypical chronic myeloid leukemia was also distributed with different proportion of Cd34_mNeu, Rps19_Neu, and Isg15_Neu, in addition to IL-7R + neurophils and Lgals1_mNeu (accounting for almost all the subgroup species of GATA2 defense). Compared with the anti-NMDA receptor encephalitis and the amyotrophic lateral sclerosis, which belong to brain diseases, Lgals1_mNeu accounted for the vast majority of the amyotrophic lateral sclerosis, while the proportion of IL-7R + neutrals in anti-NMDA receiver encephalitis was significantly increased. The distribution of neutrophil subpopulations in diffuse gas adenocarcinoma and gas cancer was similar, as well as crohn ileitis and colonial cancer. Compared with the end stage renal failure and the clear cell renal failure, the neutrophil subgroup of the end stage renal failure was dominated by Lgals1_mNeu, while the main cell subgroups of clear cell renal carcinoma were IL-7R + neutrals, Lgals1_mNeu and CD74_Neu. The distribution of subgroups of familial hypercholesterolemia and blastoma whose specimens were both obtained from liver was also significantly different. The former expressed almost exclusively Lgals1_mNeu, while the main cell group of the latter was APOA2_Neu (Fig. 4a, Table S17).

Finally, we focused on the specificity of the above cell subsets (Fig. 4b. Rps19_Neu, Anxa1_Neu and Classical_Neu had high specificity in the atypical chronic myeloid leukemia, but they were not expressed in GATA2 deficiency. Neu_c4_RSAD2, Isg15_Neu, IFITM2_Neu, CCL6_Neu, and Cd34_mNeu were assessed as specific panels (OER10 > 83%, data was not shown) in anti-NMDA receiver encephalitis, which was significantly different from that of amyotrophic lateral sclerosis. Interestingly, the specificity of several subpopulations has changed between diffuse gas adenocarcinoma and gas cancer. Rps19_Neuwas associated panel in the former, and was reference panel in the latter. Anxa1_Neu and Classical_Neu were reference panels in diffuse gas adenocarcinoma, and their specificity in gas cancer was increased to associate panels. The subgroups specificity also changed in the comparison between crohn ileitis and colorectal cancer, where Circulating neutrophils, Psap_Neu, Bladder_Neu, Anxa1_Neu, and SPP1_Neu were more prominent. Last but not the least, the specificity of Cd34_mNeu in clear cell renal cancer was higher than that in end stage renal failure (OER10 = 96.77%). These results suggested that the OER-based cell type assessment pattern could be applied to various disease types, not just lung diseases. Because of the specificity of neutrophil distribution in some types of diseases, we speculate that this evaluation system can assist in the identification of disease subtypes to a certain extent.