Raman cell sorting for single-cell research.

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Precision isolation and cultivation of single cells by vortex and flat-top laser ejection.

Single-cell isolation stands as a critical step in single-cell studies, and single-cell ejection technology based on laser induced forward transfer technology (LIFT) is considered one of the most promising methods in this regard for its ability of visible isolating single cell from complex samples. In this study, we improve the LIFT technology and introduce optical vortex laser-induced forward transfer (OV-LIFT) and flat-top laser-induced forward transfer (FT-LIFT) by utilizing spatial light modulator (SLM), aiming to enhance the precision of single-cell sorting and the cell's viability after ejection. Experimental results demonstrate that applying vortex and flat-top beams during the sorting and collection process enables precise retrieval of single cells within diameter ranges of 50 µm and 100 µm, respectively. The recovery rates of Saccharomyces cerevisiae and Escherichia coli DH5α single cell ejected by vortex beam are 89 and 78%, by flat-top beam are 85 and 57%. When employing Gaussian beam sorting, the receiving range extends to 400 µm, with cultivation success rates of S. cerevisiae and E. coli DH5α single cell are 48 and 19%, respectively. This marks the first application of different mode beams in the ejection and cultivation of single cells, providing a novel and effective approach for the precise isolation and improving the viability of single cells.

Deep learning-based ultra-fast identification of Raman spectra with low signal-to-noise ratio.

Ensuring the correct use of cell lines is crucial to obtaining reliable experimental results and avoiding unnecessary waste of resources. Raman spectroscopy has been confirmed to be able to identify cell lines, but the collection time is usually 10-30 s. In this study, we acquired Raman spectra of five cell lines with integration times of 0.1 and 8 s, respectively, and the average accuracy of using long-short memory neural network to identify the spectra of 0.1 s was 95%, and the average accuracy of identifying the spectra of 8 s was 99.8%. At the same time, we performed data enhancement of 0.1 s spectral data by real-valued non-volume preserving method, and the recognition average accuracy of long-short memory neural networks recognition of the enhanced spectral data was improved to 96.2%. With this method, we shorten the acquisition time of Raman spectra to 1/80 of the original one, which greatly improves the efficiency of cell identification.

Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment.

Rapid identification of fermented lactic acid bacteria has long been a challenge in the brewing industry. This study combined label-free surface-enhanced Raman scattering (SERS) and optical tweezer technology to construct a test platform within a microfluidic environment. Six kinds of lactic acid bacteria common in industry were tested to prove the stability of the SERS spectra. The results demonstrated that the utilization of optical tweezers to securely hold the bacteria significantly enhanced the stability of the SERS spectra. Furthermore, SVM and XGBoost machine learning algorithms were utilized to analyze the obtained Raman spectra for identification, and the identification accuracies exceeded 95% for all tested lactic acid bacteria. The findings of this study highlight the crucial role of optical tweezers in improving the stability of SERS spectra by capturing bacteria in a microfluidic environment, prove that this technology could be used in the rapid identification of lactic acid bacteria, and show great significance in expanding the applicability of the SERS technique for other bacterial testing purposes.

Evaluation of Raman spectroscopy combined with the gated recurrent unit serum detection method in early screening of gastrointestinal cancer.

Gastric and colorectal cancers are significant causes of human mortality. Conventionally, the diagnosis of gastrointestinal tumors has been accomplished through image-based techniques, including endoscopic and biopsy procedures coupled with tissue staining. Most of these methods are invasive. In contrast, Raman spectroscopy has the advantages of being non-invasive and label-free and requiring no additional reagents, making it a potential tool for the detection of serum components. In this study, we collected Raman spectra of serum samples from patients with gastric cancer (n = 93) and colorectal cancer (n = 92) and from healthy individuals (n = 100). Analysis of Raman peak areas revealed that cancer patients had significantly higher peak areas at around 2923 cm-1 compared to normal individuals, which corresponded to the presence of lipids and proteins. We successfully achieved the early screening of gastrointestinal tumors using the improved gated recurrent unit (GRU) algorithm and traditional machine learning methods. The accuracy of identifying digestive tract tumors using different recognition models exceeds 84.72%, with support vector machine (SVM) and GRU achieving 100% accuracy. The use of GRU further demonstrated its ability to differentiate subtypes of gastric and colorectal cancers based on the degree of differentiation and stage, with a recognition accuracy exceeding 95%, which is challenging using traditional machine learning methods. Furthermore, our study revealed that principal component analysis (PCA) dimensionality reduction has a limited impact on the recognition results obtained using different recognition models.

Genetically predicted vitamin C levels significantly affect patient survival and immunotypes in multiple cancer types.

Background: Recent observational studies and meta-analyses have shown that vitamin C reduces cancer incidence and mortality, but the underlying mechanisms remain unclear. We conducted a comprehensive pan-cancer analysis and biological validation in clinical samples and animal tumor xenografts to understand its prognostic value and association with immune characteristics in various cancers. Methods: We used the Cancer Genome Atlas gene expression data involving 5769 patients and 20 cancer types. Vitamin C index (VCI) was calculated using the expression of 11 genes known to genetically predict vitamin C levels, which were classified into high and low subgroups. The correlation between VCI and patient overall survival (OS), tumor mutational burden (TMB), microsatellite instability (MSI), and immune microenvironment was evaluated, using Kaplan-Meier analysis method and ESTIMATE (https://bioinformatics.mdanderson.org/estimate/). Clinical samples of breast cancer and normal tissues were used to validate the expression of VCI-related genes, and animal experiments were conducted to test the impact of vitamin C on colon cancer growth and immune cell infiltration. Results: Significant changes in expression of VCI-predicted genes were observed in multiple cancer types, especially in breast cancer. There was a correlation of VCI with prognosis in all samples (adjusted hazard ratio [AHR] = 0.87; 95% confidence interval [CI] = 0.78-0.98; P = 0.02). The specific cancer types that exhibited significant correlation between VCI and OS included breast cancer (AHR = 0.14; 95% CI = 0.05-0.40; P < 0.01), head and neck squamous cell carcinoma (AHR = 0.20; 95% CI = 0.07-0.59; P < 0.01), kidney clear cell carcinoma (AHR = 0.66; 95% CI = 0.48-0.92; P = 0.01), and rectum adenocarcinoma (AHR = 0.01; 95% CI = 0.001-0.38; P = 0.02). Interestingly, VCI was correlated with altered immunotypes and associated with TMB and MSI negatively in colon and rectal adenocarcinoma (P < 0.001) but positively in lung squamous cell carcinoma (P < 0.05). In vivo study using mice bearing colon cancer xenografts demonstrated that vitamin C could inhibit tumor growth with significant impact on immune cell infiltration. Conclusion: VCI is significantly correlated with OS and immunotypes in multiple cancers, and vitamin C might have therapeutic potential in colon cancer.

Classification of deep-sea cold seep bacteria by transformer combined with Raman spectroscopy.

Raman spectroscopy is a rapid analysis method of biological samples without labeling and destruction. At present, the commonly used Raman spectrum classification models include CNN, RNN, etc. The transformer has not been used for Raman spectrum identification. This paper introduces a new method of transformer combined with Raman spectroscopy to identify deep-sea cold seep microorganisms at the single-cell level. We collected the Raman spectra of eight cold seep bacteria, each of which has at least 500 spectra for the training of transformer model. We compare the transformer classification model with other deep learning classification models. The experimental results show that this method can improve the accuracy of microbial classification. Our average isolation level accuracy is more than 97%.

Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy.

Cell misuse and cross-contamination can affect the accuracy of cell research results and result in wasted time, manpower and material resources. Thus, cell line identification is important and necessary. At present, the commonly used cell line identification methods need cell staining and culturing. There is therefore a need to develop a new method for the rapid and automated identification of cell lines. Raman spectroscopy has become one of the emerging techniques in the field of microbial identification, with the advantages of being rapid and noninvasive and providing molecular information for biological samples, which is beneficial in the identification of cell lines. In this study, we built a library of Raman spectra for gastric mucosal epithelial cell lines GES-1 and gastric cancer cell lines, such as AGS, BGC-823, HGC-27, MKN-45, MKN-74 and SNU-16. Five spectral datasets were constructed using spectral data and included the full spectrum, fingerprint region, high-wavelength number region and Raman background of Raman spectra. A stacking ensemble learning model, SL-Raman, was built for different datasets, and gastric cancer cell identification was achieved. For the gastric cancer cells we studied, the differentiation accuracy of SL-Raman was 100% for one of the gastric cancer cells and 100% for six of the gastric cancer cells. Additionally, the separation accuracy for two gastric cancer cells with different degrees of differentiation was 100%. These results demonstrate that Raman spectroscopy combined with SL-Raman may be a new method for the rapid and accurate identification of gastric cancer. In addition, the accuracy of 94.38% for classifying Raman spectral background data using machine learning demonstrates that the Raman spectral background contains some useful spectral features. These data have been overlooked in previous studies.

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning.

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the "fingerprint" of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level.

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.

Rapid detection of beer spoilage bacteria based on label-free SERS technology.

Beer spoilage bacteria have been a headache for major breweries. In order to rapidly identify spoilage bacteria and improve the sensitivity and signal-to-noise ratio of bacterial SERS detection, the label-free SERS technique was used as a starting point, and we found eight bacteria species that led to beer spoilage. The impact of AgNP concentration and AgNP and bacterial binding time on the final results were thoroughly investigated. To maximize the increase in the SERS signal, an aluminized chip was created. We merged the t-SNE reduced dimensional analysis algorithm, and SVM, KNN, and LDA machine learning algorithms to further investigate the effect of the approach on the final identification rate. The results demonstrate that SERS spectra had an increased intensity and signal-to-noise ratio. The machine learning classification accuracy rates were all above 90%, indicating that the bacteria were correctly classified and identified.

Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis.

Gastric cancer is usually diagnosed at late stage and has a high mortality rate, whereas early detection of gastric cancer could bring a better prognosis. Conventional gastric cancer diagnostic methods suffer from long diagnostic times, severe trauma, and a high rate of misdiagnosis and rely heavily on doctors' subjective experience. Raman spectroscopy is a label-free molecular vibrational spectroscopy technique that identifies the molecular fingerprint of various samples based on the inelastic scattering of monochromatic light. Because of its advantages of non-destructive, rapid, and accurate detection, Raman spectroscopy has been widely studied for benign and malignant tumor differentiation, tumor subtype classification, and section pathology diagnosis. This paper reviews the applications of Raman spectroscopy for the in vivo and in vitro diagnosis of gastric cancer, methodology related to the spectroscopy data analysis, and presents the limitations of the technique.

Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria.

Rapid identification of marine microorganisms is critical in marine ecology, and Raman spectroscopy is a promising means to achieve this. Single cell Raman spectra contain the biochemical profile of a cell, which can be used to identify cell phenotype through classification models. However, traditional classification methods require a substantial reference database, which is highly challenging when sampling at difficult-to-access locations. In this scenario, only a few spectra are available to create a taxonomy model, making qualitative analysis difficult. And the accuracy of classification is reduced when the signal-to-noise ratio of a spectrum is low. Here, we describe a novel method for categorizing microorganisms that combines optical tweezers Raman spectroscopy, Progressive Growing of Generative Adversarial Nets (PGGAN), and Residual network (ResNet) analysis. Using the optical Raman tweezers, we acquired single cell Raman spectra from five deep-sea bacterial strains. We randomly selected 300 spectra from each strain as the database for training a PGGAN model. PGGAN generates a large number of high-resolution spectra similar to the real data for the training of the residual neural network. Experimental validations show that the method enhances machine learning classification accuracy while also reducing the demand for a considerable amount of training data, both of which are advantageous for analyzing Raman spectra of low signal-to-noise ratios. A classification model was built with this method, which reduces the spectra collection time to 1/3 without compromising the classification accuracy.

Isolation and Culture of Single Microbial Cells by Laser Ejection Sorting Technology.

Single-cell isolation and cultivation play an important role in studying physiology, gene expression, and functions of microorganisms. A series of single-cell isolation technologies have been developed, among which single-cell ejection technology is one of the most promising. Single-cell ejection technology has applied laser-induced forward transfer (LIFT) techniques to isolate bacteria, but the viability (or recovery rate) of cells after sorting has not been clarified in current research. In this work, to keep the cells alive as long as possible, we propose a three-layer LIFT system (top layer, 25-nm aluminum film; second layer, 3 µm agar media; third layer, liquid containing bacteria) for the isolation and cultivation of single Gram-negative (Escherichia coli), Gram-positive (Lactobacillus rhamnosus GG [LGG]), and eukaryotic (Saccharomyces cerevisiae) microorganisms. The experiment results showed that the average survival rates for ejected pure single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for LGG. In addition, we successfully isolated and cultured the green fluorescent protein (GFP)-expressing E. coli JM109 from a mixture containing complex communities of soil bacteria by fluorescence signal. The average survival rate of E. coli JM109 was demonstrated to be 25.3%. In this study, the isolated and cultured single colonies were further confirmed by colony PCR and sequencing. Such precise sorting and cultivation techniques of live single microbial cells could be coupled with other microscopic approaches to isolate single microorganisms with specific functions, revealing their roles in the natural community. IMPORTANCE We developed a laser-induced forward transfer (LIFT) technology to accurately isolate single live microbial cells. The cultivation recovery rates of the ejected single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for Lactobacillus rhamnosus GG (LGG). With coupled LIFT with a fluorescence microscope, we demonstrated that single cells of GFP-expressing E. coli JM109 were sorted according to fluorescence signal from a complex community of soil bacteria and subsequently cultured with 25% cultivation recovery rate. This single-cell live sorting technology could isolate single microbes with specific functions, revealing their roles in the natural community.

MicroRNA­192 acts as a tumor suppressor in colon cancer and simvastatin activates miR­192 to inhibit cancer cell growth.

Colon cancer is one of the most common malignant tumors worldwide. Understanding the underlying molecular mechanisms is crucial for the development of therapeutic strategies for the treatment of patients with colon cancer. In the present study, a novel tumor suppressive microRNA, miR­192, was demonstrated to be markedly downregulated in colon cancer cells compared with normal colon cells. By overexpressing miR­192 in colon cancer HCT­116 cells, the results of the present study revealed that miR­192 inhibits cell proliferation, migration and invasion. Bioinformatics were used to determine the target gene of miR­192 and Ras­related protein Rab­2A (RAB2A) was identified as a downstream target of miR­192. Following the determination of the role of the miR­192­RAB2A pathway in colon cancer, small molecules that may regulate miR­192 were screened and the results demonstrated that simvastatin is an activator of miR­192. Furthermore, simvastatin upregulated miR­192 and inhibited the expression of downstream targets of miR­192, which subsequently led to suppressed proliferation, migration and invasion of colon cancer cells. In conclusion, the present study identified a novel colon cancer cell suppressor, as well as a small­molecule activator of the tumor suppressor miR­192, which may represent a therapeutic strategy for the treatment of patients with colon cancer.

[Association between polymorphisms of HIF-1alpha C1772T and G1790A and hypoxic acclimation in high altitude in Tibetans].

In this study the blood sample was collected from eighty-six athletes in Tibetan mountaining team and Tibetan mountaining sports school and ninety healthy Han nationality people in Guangdong province, and genomic DNA was extracted from peripheral leukocyte. The allele frequency distribution and the genotypes combination distribution of hypoxia-inducible factor-la gene (HIF-1alpha)exonl2 C1772T and G1790A were examined by restriction fragment length polymorphism PCR (PCR-RFLP) in order to evaluate the association of single nucleotide polymorphisms (SNPs) of HIF-1alpha C1772T and G1790A with hypoxic acclimation in high altitude in Tibetans. The results indicated that the genotype frequency of HIF-1alpha C1772T in Tibetan and in Han nationality was 13.95% versus 16.67% in genotype CC, 38.37% versus 41.11% in genotype CT and 47.68% versus 42.22% in genotype TT. No significant difference in CC, CT and TT genotype frequency of HIF-1alphaC1772T was shown between Tibetans and Han nationality respectively, while GA genotype frequency of HIF-la G1790A in Tibetans was higher than that in Han nationality. The GA genotype of HIF-1alpha G1790A may be involved in the hypoxic acclimation of high altitude , and it is worth of deep-going investigation.

[Relationship between adaptation to high altitude hypoxia environment and glucose transport 1 gene polymorphism].

This investigation was conducted to explore the relationship between the polymorphism of gene of glucose transport 1(GLUT1) and the human body adaptation to high altitude hypoxia environment. The data on glucose transport 1 gene polymorphism in Tibetan mountaineers (high altitude group) were analyzed and compared with the data from the level-land Guangdong Hans (control group). The genotype of 86 Tibetan mountaineers and 90 level-land Hans as controls were tested with polymerase chain reaction followed by restriction fragment length polymorphism analysis for GLUT1 gene. The results showed that, in the high altitude mountaineer group, the frequencies of +22999 locus genotypes GG, GT and TT were 44.2%, 46.5% and 9.3% respectively, and such frequencies in the control group were 66.7%, 31.1% and 2.2% respectively. The frequencies of + 22999 polymorphic genotypes and alleles showed statistically significant difference between the high altitude group and the control group (P<0.05). Genetic single nucleotide polymorphism in GLUT1 G+22999T may be associated with the adaptation to high altitude hypoxia.

[Association of polymorphisms of 1772 (C-->T) and 1790 (G-->A) in HIF1A gene with hypoxia adaptation in high altitude in Sherpas].

OBJECTIVE: To investigate the association of single nucleotide polymorphisms (SNPs) of 1772 (C-->T) and 1790 (G-->A) in exon 12 of hypoxia-inducible factor 1, alpha subunit gene (HIF1A) with hypoxia adaptation in high altitude in Sherpas. METHODS: The blood samples were chosen from 148 Sherpas in Tibet high altitude and 90 Han nationality healthy people in Guangdong province, and from which genomic DNA was extracted. The single nucleotide polymorphisms of 1772(C-->T) and 1790(G-->A) in exon 12 of HIF1A gene were examined by restriction fragment length polymorphism-polymerase chain reaction (RFLP-PCR). RESULTS: The genotype frequency of HIF1A gene 1790 (G-->A) in Sherpas and Han nationality was 57.43% versus 75.56% in GG genotype, 37.84% versus 21.11% in GA genotype and 4.73% versus 3.33% in AA genotype. GG genotype frequency in Sherpas was lower than that in Han nationality (P<0.01), while GA genotype frequency in Sherpas was higher than that in Han nationality (P<0.01). No significant difference in CC, CT and TT genotype frequency of 1772(C-->T) was shown between two groups respectively. The total frequency of CC + GA, CT + AA, TT + GA and TT + AA in Sherpas was higher than that in Han nationality. CONCLUSION: Polymorphisms of HIF1A gene 1790 (G-->A) are associated with hypoxia adaptation in high altitude in Sherpas. GA and AA genotype may be benefit to hypoxia adaptation, and it is worthy of deep-going investigation.

[Single nucleotide polymorphisms study of surfactant protein A gene between Tibet Sherpas and Guangdong Chinese Hans].

AIM: To investigate the relationship between single nucleotide polymorphisms(SNPs) of surfactant protein A(SP-A) gene of Chinese Han and Sherpas and their adaptation to high altitude hypoxia. METHODS: The genotypes of 90 Chinese Han in Guangdong and 104 Sherpas in Tibet were analyzed by sequence special primer polymerase chain reaction(SSP-PCR) sequencing the surfactant protein A gene. RESULTS: The frequencies of genotypes and alleles at SP-A1 1544 locus showed no difference between the Sherpas and the Chinese Han (P>0.05). However, the frequencies of genotypes C/C, C/T and T/T at SP-A1 3241 locus were 75.0%, 22.1% and 2.9%, respectively in Sherpas, difference to Han population, they were 50.0%, 35.6% and 14.4%, respectirely(P<0.05). Whilst in Sherpas allele frequencies of C and T were 86.1% and 13.9% respectively but they were 67.8% and 32.2% respectively in the Chinese Han(P<0.05); The frequencies of C/C, A/C and A/A at SP-A2 3265 locus were 37.5%, 53.8%, and 8.7%, respectively in the Sherpas were also difference to Chinese Han, they were 63.3%, 30.0%, and 6.7%, respectively. Whilst allele frequencies of C and A were 64.4% and 35.6% in Sherpas but 78.3% and 21.7% in Chinese Han, which showed statistically difference between two groups(P<0.05). CONCLUSION: There were statistically differences of genotypes and alleles at SP-A2 3265 locus in Hans and Sherpas. SNP in SP-A2 at 3265 may be related to the adaptation of Sherpas to high altitude hypoxia.