#METABOLOMICS WORKBENCH SBlab_20241229_225507 DATATRACK_ID:5495 STUDY_ID:ST003654 ANALYSIS_ID:AN006004 PROJECT_ID:PR002264 VERSION 1 CREATED_ON January 8, 2025, 2:07 am #PROJECT PR:PROJECT_TITLE Identifying the lipidomic signature from human blood reveals human sex. PR:PROJECT_SUMMARY We used paper spray ionization mass spectrometry (PSI-MS) to develop a rapid PR:PROJECT_SUMMARY reagent-free blood test for human sex identification by analyzing PR:PROJECT_SUMMARY sex-discriminatory lipid profiles from 200 dried blood spot (DBS) samples PR:PROJECT_SUMMARY comprising 100 males and 100 females. Following an in-house Python-based PR:PROJECT_SUMMARY peak-picking program, we could identify 664 prominent peaks (m/z values) from PR:PROJECT_SUMMARY the TIC (Total Ion Current) normalized positive ion mode PSI-MS dataset PR:PROJECT_SUMMARY comprised of all DBS samples on paper. We performed a supervised machine PR:PROJECT_SUMMARY learning analysis on all detected lipid signals to identify sex biomarkers PR:PROJECT_SUMMARY within the dataset. This study revealed significant differences in the level of PR:PROJECT_SUMMARY specific sphingomyelin and phospholipid species between male and female DBS PR:PROJECT_SUMMARY samples. PR:INSTITUTE Indian Institute of Science Education and Research Tirupati PR:DEPARTMENT Chemistry PR:LABORATORY SBlab PR:LAST_NAME Mondal PR:FIRST_NAME Supratim PR:ADDRESS Venkatagiri Road, Yerpedu Mandal, Tirupati, Andhra Pradesh - 517619, India PR:EMAIL supratimmondal@students.iisertirupati.ac.in PR:PHONE 9051034727 #STUDY ST:STUDY_TITLE Rapid and Reagent-free Analysis of Dried Blood Spot by Paper Spray Mass ST:STUDY_TITLE Spectrometry Reveals Sex: Implications in Forensic Investigations. ST:STUDY_SUMMARY Identifying sex from an unknown dried blood spot (DBS), especially when the ST:STUDY_SUMMARY corpse remains undiscovered, often provides valuable evidence in forensic ST:STUDY_SUMMARY casework. While DNA-based sex determination is a reliable method in forensic ST:STUDY_SUMMARY settings, it requires ex-pensive reagents and is time-consuming. To develop a ST:STUDY_SUMMARY rapid reagent-free blood test for sex, we employed paper spray ionization mass ST:STUDY_SUMMARY spectrometry (PSI-MS) to capture sex-discriminatory lipid profiles from 200 DBS ST:STUDY_SUMMARY samples comprising 100 males and 100 females. We conducted a supervised machine ST:STUDY_SUMMARY learning (ML) analysis on all detected lipid signals to hunt biomarkers of sex ST:STUDY_SUMMARY within the dataset. This analysis unveiled significant differences in specific ST:STUDY_SUMMARY sphingomyelin and phospholipid species levels between male and female DBS ST:STUDY_SUMMARY samples. Using the parsimonious set of 60 lipid signals, we constructed a ST:STUDY_SUMMARY classifier that achieved 100% overall accuracy in predicting sex from DBS on ST:STUDY_SUMMARY paper. Additionally, we assessed three-day-old air-exposed DBS on glass and ST:STUDY_SUMMARY granite surfaces, simulating the typical blood samples available for forensic ST:STUDY_SUMMARY investigations. Consequently, we achieved ~92% overall sex prediction accuracy ST:STUDY_SUMMARY from the holdout test dataset of DBS on glass and granite surfaces. As a highly ST:STUDY_SUMMARY sensitive detection tool, PSI-MS combined with ML has the potential to ST:STUDY_SUMMARY revolutionize forensic methods by rapidly analyzing blood molecules encoding ST:STUDY_SUMMARY personal information. ST:INSTITUTE Indian Institute of Science Education and Research Tirupati ST:DEPARTMENT Chemistry ST:LABORATORY SBlab ST:LAST_NAME Mondal ST:FIRST_NAME Supratim ST:ADDRESS Venkatagiri Road, Yerpedu Mandal, Tirupati, Andhra Pradesh - 517619, India ST:EMAIL supratimmondal@students.iisertirupati.ac.in ST:PHONE 9051034727 ST:TOTAL_SUBJECTS 200 ST:NUM_MALES 100 ST:NUM_FEMALES 100 #SUBJECT SU:SUBJECT_TYPE Human SU:SUBJECT_SPECIES Homo sapiens SU:TAXONOMY_ID 9606 SU:GENDER Male and female #SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Raw file names and additional sample data SUBJECT_SAMPLE_FACTORS H1 HF1 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF1.mzML SUBJECT_SAMPLE_FACTORS H2 HF2 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF2.mzML SUBJECT_SAMPLE_FACTORS H3 HF3 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF3.mzML SUBJECT_SAMPLE_FACTORS H4 HF4 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF4.mzML SUBJECT_SAMPLE_FACTORS H5 HF5 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF5.mzML SUBJECT_SAMPLE_FACTORS H6 HF6 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF6.mzML SUBJECT_SAMPLE_FACTORS H7 HF7 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF7.mzML SUBJECT_SAMPLE_FACTORS H8 HF8 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF8.mzML SUBJECT_SAMPLE_FACTORS H9 HF9 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF9.mzML SUBJECT_SAMPLE_FACTORS H10 HF10 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF10.mzML SUBJECT_SAMPLE_FACTORS H11 HF11 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF11.mzML SUBJECT_SAMPLE_FACTORS H12 HF12 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF12.mzML SUBJECT_SAMPLE_FACTORS H13 HF13 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF13.mzML SUBJECT_SAMPLE_FACTORS H14 HF14 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF14.mzML SUBJECT_SAMPLE_FACTORS H15 HF15 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF15.mzML SUBJECT_SAMPLE_FACTORS H16 HF16 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF16.mzML SUBJECT_SAMPLE_FACTORS H17 HF17 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF17.mzML SUBJECT_SAMPLE_FACTORS H18 HF18 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF18.mzML SUBJECT_SAMPLE_FACTORS H19 HF19 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF19.mzML SUBJECT_SAMPLE_FACTORS H20 HF20 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF20.mzML SUBJECT_SAMPLE_FACTORS H21 HF21 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF21.mzML SUBJECT_SAMPLE_FACTORS H22 HF22 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF22.mzML SUBJECT_SAMPLE_FACTORS H23 HF23 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF23.mzML SUBJECT_SAMPLE_FACTORS H24 HF24 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF24.mzML SUBJECT_SAMPLE_FACTORS H25 HF25 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF25.mzML SUBJECT_SAMPLE_FACTORS H26 HF26 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF26.mzML SUBJECT_SAMPLE_FACTORS H27 HF27 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF27.mzML SUBJECT_SAMPLE_FACTORS H28 HF28 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF28.mzML SUBJECT_SAMPLE_FACTORS H29 HF29 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF29.mzML SUBJECT_SAMPLE_FACTORS H30 HF30 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF30.mzML SUBJECT_SAMPLE_FACTORS H31 HF31 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF31.mzML SUBJECT_SAMPLE_FACTORS H32 HF32 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF32.mzML SUBJECT_SAMPLE_FACTORS H33 HF33 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF33.mzML SUBJECT_SAMPLE_FACTORS H34 HF34 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF34.mzML SUBJECT_SAMPLE_FACTORS H35 HF35 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF35.mzML SUBJECT_SAMPLE_FACTORS H36 HF36 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF36.mzML SUBJECT_SAMPLE_FACTORS H37 HF37 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF37.mzML SUBJECT_SAMPLE_FACTORS H38 HF38 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF38.mzML SUBJECT_SAMPLE_FACTORS H39 HF39 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF39.mzML SUBJECT_SAMPLE_FACTORS H40 HF40 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF40.mzML SUBJECT_SAMPLE_FACTORS H41 HF41 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF41.mzML SUBJECT_SAMPLE_FACTORS H42 HF42 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF42.mzML SUBJECT_SAMPLE_FACTORS H43 HF43 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF43.mzML SUBJECT_SAMPLE_FACTORS H44 HF44 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF44.mzML SUBJECT_SAMPLE_FACTORS H45 HF45 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF45.mzML SUBJECT_SAMPLE_FACTORS H46 HF46 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF46.mzML SUBJECT_SAMPLE_FACTORS H47 HF47 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF47.mzML SUBJECT_SAMPLE_FACTORS H48 HF48 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF48.mzML SUBJECT_SAMPLE_FACTORS H49 HF49 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF49.mzML SUBJECT_SAMPLE_FACTORS H50 HF50 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF50.mzML SUBJECT_SAMPLE_FACTORS H51 HF51 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF51.mzML SUBJECT_SAMPLE_FACTORS H52 HF52 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF52.mzML SUBJECT_SAMPLE_FACTORS H53 HF53 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF53.mzML SUBJECT_SAMPLE_FACTORS H54 HF54 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF54.mzML SUBJECT_SAMPLE_FACTORS H55 HF55 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF55.mzML SUBJECT_SAMPLE_FACTORS H56 HF56 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF56.mzML SUBJECT_SAMPLE_FACTORS H57 HF57 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF57.mzML SUBJECT_SAMPLE_FACTORS H58 HF58 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF58.mzML SUBJECT_SAMPLE_FACTORS H59 HF59 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF59.mzML SUBJECT_SAMPLE_FACTORS H60 HF60 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF60.mzML SUBJECT_SAMPLE_FACTORS H61 HF61 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF61.mzML SUBJECT_SAMPLE_FACTORS H62 HF62 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF62.mzML SUBJECT_SAMPLE_FACTORS H63 HF63 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF63.mzML SUBJECT_SAMPLE_FACTORS H64 HF64 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF64.mzML SUBJECT_SAMPLE_FACTORS H65 HF65 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF65.mzML SUBJECT_SAMPLE_FACTORS H66 HF66 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF66.mzML SUBJECT_SAMPLE_FACTORS H67 HF67 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF67.mzML SUBJECT_SAMPLE_FACTORS H68 HF68 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF68.mzML SUBJECT_SAMPLE_FACTORS H69 HF69 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF69.mzML SUBJECT_SAMPLE_FACTORS H70 HF70 Sample source:Blood | Sex:Female RAW_FILE_NAME=HF70.mzML SUBJECT_SAMPLE_FACTORS H71 HF71_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF71_glass.mzML SUBJECT_SAMPLE_FACTORS H72 HF72_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF72_glass.mzML SUBJECT_SAMPLE_FACTORS H73 HF73_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF73_glass.mzML SUBJECT_SAMPLE_FACTORS H74 HF74_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF74_glass.mzML SUBJECT_SAMPLE_FACTORS H75 HF75_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF75_glass.mzML SUBJECT_SAMPLE_FACTORS H76 HF76_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF76_glass.mzML SUBJECT_SAMPLE_FACTORS H77 HF77_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF77_glass.mzML SUBJECT_SAMPLE_FACTORS H78 HF78_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF78_glass.mzML SUBJECT_SAMPLE_FACTORS H79 HF79_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF79_glass.mzML SUBJECT_SAMPLE_FACTORS H80 HF80_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF80_glass.mzML SUBJECT_SAMPLE_FACTORS H81 HF81_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF81_glass.mzML SUBJECT_SAMPLE_FACTORS H82 HF82_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF82_glass.mzML SUBJECT_SAMPLE_FACTORS H83 HF83_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF83_glass.mzML SUBJECT_SAMPLE_FACTORS H84 HF84_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF84_glass.mzML SUBJECT_SAMPLE_FACTORS H85 HF85_glass Sample source:Blood | Sex:Female RAW_FILE_NAME=HF85_glass.mzML SUBJECT_SAMPLE_FACTORS H86 HF86_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF86_granite.mzML SUBJECT_SAMPLE_FACTORS H87 HF87_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF87_granite.mzML SUBJECT_SAMPLE_FACTORS H88 HF88_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF88_granite.mzML SUBJECT_SAMPLE_FACTORS H89 HF89_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF89_granite.mzML SUBJECT_SAMPLE_FACTORS H90 HF90_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF90_granite.mzML SUBJECT_SAMPLE_FACTORS H91 HF91_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF91_granite.mzML SUBJECT_SAMPLE_FACTORS H92 HF92_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF92_granite.mzML SUBJECT_SAMPLE_FACTORS H93 HF93_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF93_granite.mzML SUBJECT_SAMPLE_FACTORS H94 HF94_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF94_granite.mzML SUBJECT_SAMPLE_FACTORS H95 HF95_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF95_granite.mzML SUBJECT_SAMPLE_FACTORS H96 HF96_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF96_granite.mzML SUBJECT_SAMPLE_FACTORS H97 HF97_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF97_granite.mzML SUBJECT_SAMPLE_FACTORS H98 HF98_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF98_granite.mzML SUBJECT_SAMPLE_FACTORS H99 HF99_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF99_granite.mzML SUBJECT_SAMPLE_FACTORS H100 HF100_granite Sample source:Blood | Sex:Female RAW_FILE_NAME=HF100_granite.mzML SUBJECT_SAMPLE_FACTORS H101 HM1 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM1.mzML SUBJECT_SAMPLE_FACTORS H102 HM2 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM2.mzML SUBJECT_SAMPLE_FACTORS H103 HM3 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM3.mzML SUBJECT_SAMPLE_FACTORS H104 HM4 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM4.mzML SUBJECT_SAMPLE_FACTORS H105 HM5 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM5.mzML SUBJECT_SAMPLE_FACTORS H106 HM6 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM6.mzML SUBJECT_SAMPLE_FACTORS H107 HM7 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM7.mzML SUBJECT_SAMPLE_FACTORS H108 HM8 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM8.mzML SUBJECT_SAMPLE_FACTORS H109 HM9 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM9.mzML SUBJECT_SAMPLE_FACTORS H110 HM10 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM10.mzML SUBJECT_SAMPLE_FACTORS H111 HM11 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM11.mzML SUBJECT_SAMPLE_FACTORS H112 HM12 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM12.mzML SUBJECT_SAMPLE_FACTORS H113 HM13 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM13.mzML SUBJECT_SAMPLE_FACTORS H114 HM14 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM14.mzML SUBJECT_SAMPLE_FACTORS H115 HM15 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM15.mzML SUBJECT_SAMPLE_FACTORS H116 HM16 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM16.mzML SUBJECT_SAMPLE_FACTORS H117 HM17 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM17.mzML SUBJECT_SAMPLE_FACTORS H118 HM18 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM18.mzML SUBJECT_SAMPLE_FACTORS H119 HM19 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM19.mzML SUBJECT_SAMPLE_FACTORS H120 HM20 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM20.mzML SUBJECT_SAMPLE_FACTORS H121 HM21 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM21.mzML SUBJECT_SAMPLE_FACTORS H122 HM22 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM22.mzML SUBJECT_SAMPLE_FACTORS H123 HM23 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM23.mzML SUBJECT_SAMPLE_FACTORS H124 HM24 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM24.mzML SUBJECT_SAMPLE_FACTORS H125 HM25 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM25.mzML SUBJECT_SAMPLE_FACTORS H126 HM26 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM26.mzML SUBJECT_SAMPLE_FACTORS H127 HM27 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM27.mzML SUBJECT_SAMPLE_FACTORS H128 HM28 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM28.mzML SUBJECT_SAMPLE_FACTORS H129 HM29 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM29.mzML SUBJECT_SAMPLE_FACTORS H130 HM30 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM30.mzML SUBJECT_SAMPLE_FACTORS H131 HM31 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM31.mzML SUBJECT_SAMPLE_FACTORS H132 HM32 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM32.mzML SUBJECT_SAMPLE_FACTORS H133 HM33 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM33.mzML SUBJECT_SAMPLE_FACTORS H134 HM34 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM34.mzML SUBJECT_SAMPLE_FACTORS H135 HM35 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM35.mzML SUBJECT_SAMPLE_FACTORS H136 HM36 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM36.mzML SUBJECT_SAMPLE_FACTORS H137 HM37 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM37.mzML SUBJECT_SAMPLE_FACTORS H138 HM38 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM38.mzML SUBJECT_SAMPLE_FACTORS H139 HM39 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM39.mzML SUBJECT_SAMPLE_FACTORS H140 HM40 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM40.mzML SUBJECT_SAMPLE_FACTORS H141 HM41 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM41.mzML SUBJECT_SAMPLE_FACTORS H142 HM42 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM42.mzML SUBJECT_SAMPLE_FACTORS H143 HM43 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM43.mzML SUBJECT_SAMPLE_FACTORS H144 HM44 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM44.mzML SUBJECT_SAMPLE_FACTORS H145 HM45 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM45.mzML SUBJECT_SAMPLE_FACTORS H146 HM46 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM46.mzML SUBJECT_SAMPLE_FACTORS H147 HM47 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM47.mzML SUBJECT_SAMPLE_FACTORS H148 HM48 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM48.mzML SUBJECT_SAMPLE_FACTORS H149 HM49 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM49.mzML SUBJECT_SAMPLE_FACTORS H150 HM50 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM50.mzML SUBJECT_SAMPLE_FACTORS H151 HM51 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM51.mzML SUBJECT_SAMPLE_FACTORS H152 HM52 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM52.mzML SUBJECT_SAMPLE_FACTORS H153 HM53 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM53.mzML SUBJECT_SAMPLE_FACTORS H154 HM54 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM54.mzML SUBJECT_SAMPLE_FACTORS H155 HM55 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM55.mzML SUBJECT_SAMPLE_FACTORS H156 HM56 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM56.mzML SUBJECT_SAMPLE_FACTORS H157 HM57 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM57.mzML SUBJECT_SAMPLE_FACTORS H158 HM58 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM58.mzML SUBJECT_SAMPLE_FACTORS H159 HM59 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM59.mzML SUBJECT_SAMPLE_FACTORS H160 HM60 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM60.mzML SUBJECT_SAMPLE_FACTORS H161 HM61 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM61.mzML SUBJECT_SAMPLE_FACTORS H162 HM62 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM62.mzML SUBJECT_SAMPLE_FACTORS H163 HM63 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM63.mzML SUBJECT_SAMPLE_FACTORS H164 HM64 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM64.mzML SUBJECT_SAMPLE_FACTORS H165 HM65 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM65.mzML SUBJECT_SAMPLE_FACTORS H166 HM66 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM66.mzML SUBJECT_SAMPLE_FACTORS H167 HM67 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM67.mzML SUBJECT_SAMPLE_FACTORS H168 HM68 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM68.mzML SUBJECT_SAMPLE_FACTORS H169 HM69 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM69.mzML SUBJECT_SAMPLE_FACTORS H170 HM70 Sample source:Blood | Sex:Male RAW_FILE_NAME=HM70.mzML SUBJECT_SAMPLE_FACTORS H171 HM71_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM71_glass.mzML SUBJECT_SAMPLE_FACTORS H172 HM72_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM72_glass.mzML SUBJECT_SAMPLE_FACTORS H173 HM73_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM73_glass.mzML SUBJECT_SAMPLE_FACTORS H174 HM74_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM74_glass.mzML SUBJECT_SAMPLE_FACTORS H175 HM75_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM75_glass.mzML SUBJECT_SAMPLE_FACTORS H176 HM76_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM76_glass.mzML SUBJECT_SAMPLE_FACTORS H177 HM77_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM77_glass.mzML SUBJECT_SAMPLE_FACTORS H178 HM78_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM78_glass.mzML SUBJECT_SAMPLE_FACTORS H179 HM79_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM79_glass.mzML SUBJECT_SAMPLE_FACTORS H180 HM80_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM80_glass.mzML SUBJECT_SAMPLE_FACTORS H181 HM81_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM81_glass.mzML SUBJECT_SAMPLE_FACTORS H182 HM82_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM82_glass.mzML SUBJECT_SAMPLE_FACTORS H183 HM83_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM83_glass.mzML SUBJECT_SAMPLE_FACTORS H184 HM84_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM84_glass.mzML SUBJECT_SAMPLE_FACTORS H185 HM85_glass Sample source:Blood | Sex:Male RAW_FILE_NAME=HM85_glass.mzML SUBJECT_SAMPLE_FACTORS H186 HM86_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM86_granite.mzML SUBJECT_SAMPLE_FACTORS H187 HM87_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM87_granite.mzML SUBJECT_SAMPLE_FACTORS H188 HM88_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM88_granite.mzML SUBJECT_SAMPLE_FACTORS H189 HM89_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM89_granite.mzML SUBJECT_SAMPLE_FACTORS H190 HM90_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM90_granite.mzML SUBJECT_SAMPLE_FACTORS H191 HM91_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM91_granite.mzML SUBJECT_SAMPLE_FACTORS H192 HM92_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM92_granite.mzML SUBJECT_SAMPLE_FACTORS H193 HM93_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM93_granite.mzML SUBJECT_SAMPLE_FACTORS H194 HM94_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM94_granite.mzML SUBJECT_SAMPLE_FACTORS H195 HM95_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM95_granite.mzML SUBJECT_SAMPLE_FACTORS H196 HM96_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM96_granite.mzML SUBJECT_SAMPLE_FACTORS H197 HM97_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM97_granite.mzML SUBJECT_SAMPLE_FACTORS H198 HM98_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM98_granite.mzML SUBJECT_SAMPLE_FACTORS H199 HM99_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM99_granite.mzML SUBJECT_SAMPLE_FACTORS H200 HM100_granite Sample source:Blood | Sex:Male RAW_FILE_NAME=HM100_granite.mzML #COLLECTION CO:COLLECTION_SUMMARY We have collected finger-pricked blood from 200 volunteers (human participants) CO:COLLECTION_SUMMARY following the approval of the Human Ethics Committee and Institution Review CO:COLLECTION_SUMMARY Board (IRB). The left index fingers of all volunteers (human participants) were CO:COLLECTION_SUMMARY cleaned with the hand-rubbing alcoholic solution and dried in air for one minute CO:COLLECTION_SUMMARY prior to the blood collection. The finger was pricked using a commercially CO:COLLECTION_SUMMARY available lancing device pen with sterile disposable lancet needles. The freshly CO:COLLECTION_SUMMARY come-out fingerprick blood was immediately spotted on the previously incised CO:COLLECTION_SUMMARY triangular (isosceles with 10 mm in height and 5 mm in the base) Whatman filter CO:COLLECTION_SUMMARY paper (grade 1) and kept for drying in the open air for 5 min followed by CO:COLLECTION_SUMMARY preserving in a tightly sealed container at 4 oC. Thus, 140 human dried blood CO:COLLECTION_SUMMARY spot (DBS) samples were collected directly on the paper. Analogously, another CO:COLLECTION_SUMMARY set of blood spots was collected from 60 volunteers (30 males and 30 females) on CO:COLLECTION_SUMMARY two other solid surfaces, such as glass and granite, which were then kept for CO:COLLECTION_SUMMARY drying in the open air and at room temperature for three days before extracting CO:COLLECTION_SUMMARY for MS study. CO:SAMPLE_TYPE Blood (whole) CO:COLLECTION_METHOD Dried blood spot from finger prick blood drop CO:STORAGE_CONDITIONS 4℃ #TREATMENT TR:TREATMENT_SUMMARY No further post-sampling treatment was performed with the dried blood spot (DBS) TR:TREATMENT_SUMMARY samples. The collected DBS were directly sprayed toward the MS inlet capillary TR:TREATMENT_SUMMARY following the paper spray ionization technique. #SAMPLEPREP SP:SAMPLEPREP_SUMMARY No further steps of sample pre-treatment/preparation were followed for the dried SP:SAMPLEPREP_SUMMARY blood spot (DBS) collected on the paper sample. However, the DBS collected from SP:SAMPLEPREP_SUMMARY other solid surfaces (e.g. glass or granite) encounters another step of the SP:SAMPLEPREP_SUMMARY sample preparation. Such DBS samples were extracted in approximately 200 µL of SP:SAMPLEPREP_SUMMARY methanol (MeOH) containing 0.1% formic acid (HCOOH) solution using a pipette. In SP:SAMPLEPREP_SUMMARY this extraction process, the above methanolic solvent was dispensed onto the SP:SAMPLEPREP_SUMMARY dried blood spot. The spot was then allowed to swell for a couple of minutes. SP:SAMPLEPREP_SUMMARY After that, the liquid above the spot was repeatedly pipetted in and out SP:SAMPLEPREP_SUMMARY multiple times (at least five times) to extract the blood lipids/metabolites in SP:SAMPLEPREP_SUMMARY methanol while keeping blood cells insoluble. Thus, the ex-tracted methanolic SP:SAMPLEPREP_SUMMARY solutions from individual blood samples were collected in microcentrifuge tubes SP:SAMPLEPREP_SUMMARY for aliquoting (15 µL) on the triangular Whatman filter papers followed by SP:SAMPLEPREP_SUMMARY PSI-MS analysis. SP:PROCESSING_STORAGE_CONDITIONS 4℃ SP:EXTRACT_STORAGE 4℃ #CHROMATOGRAPHY CH:CHROMATOGRAPHY_TYPE None (Direct infusion) CH:INSTRUMENT_NAME none CH:COLUMN_NAME none CH:SOLVENT_A none CH:SOLVENT_B none CH:FLOW_GRADIENT none CH:FLOW_RATE none CH:COLUMN_TEMPERATURE none #ANALYSIS AN:ANALYSIS_TYPE MS AN:LABORATORY_NAME SBlab AN:OPERATOR_NAME Supratim Mondal #MS MS:INSTRUMENT_NAME Thermo Orbitrap Elite Hybrid Ion Trap-Orbitrap MS:INSTRUMENT_TYPE Orbitrap MS:MS_TYPE PSI MS:ION_MODE POSITIVE MS:MS_COMMENTS All the dried blood spot (DBS) samples were considered for paper spray MS:MS_COMMENTS ionization mass spectrometry (PSI-MS) scanning under positive ion mode. The MS MS:MS_COMMENTS data were extracted from each .raw file by averaging the scans over a time of MS:MS_COMMENTS 1-minute data recording. All mass spectral data were subjected to the total ion MS:MS_COMMENTS current (TIC) and mean normalization method to surpass any random effect that MS:MS_COMMENTS could influence the data and make samples comparable. Since there was no MS:MS_COMMENTS noticeable spectral difference between the average spectra (across all samples) MS:MS_COMMENTS resulting from TIC and mean normalized data in each group (data not shown), we MS:MS_COMMENTS opted to consider the TIC normalized (a widely used normalization method) data MS:MS_COMMENTS only for further data processing and analysis. In total, we collected 200 MS MS:MS_COMMENTS data from different samples. In the initial phase, 140 TIC-normalized PSI-MS MS:MS_COMMENTS data obtained from direct DBS on paper (70 male and 70 female candidates) were MS:MS_COMMENTS divided randomly into two sets using random stratified sampling. The first set MS:MS_COMMENTS (Set-I) comprised 112 samples (56 males and 56 females). Set-I data were used to MS:MS_COMMENTS pinpoint the most prominent signals (peaks) in the MS dataset and determine the MS:MS_COMMENTS most important features (peaks) for building the machine learning (ML) model. MS:MS_COMMENTS The second set (Set-II) consists of the remaining 28 samples (14 males and 14 MS:MS_COMMENTS females), which were separated and kept as a holdout test set to evaluate the MS:MS_COMMENTS performances of the final machine learning models at a later stage. In the MS:MS_COMMENTS second phase, 60 TIC-normalized PSI-MS data (30 males and 30 females) obtained MS:MS_COMMENTS from dried blood on other solid surfaces (glass and granite) were included to MS:MS_COMMENTS develop another ML classifier. Following the similar stratified sampling scheme MS:MS_COMMENTS used for the DBS on paper samples, these samples were randomly partitioned into MS:MS_COMMENTS two sets. At first, 48 samples (24 male and 24 female) were sorted following MS:MS_COMMENTS stratified sampling and referred to as Set-III. The remaining 12 samples (6 MS:MS_COMMENTS males and 6 females) were considered for another holdout test set and labeled as MS:MS_COMMENTS Set-IV. Thus, another ML model was built using the Set-I and Set-III samples, MS:MS_COMMENTS considering the previously identified features from Set-I and Set-II. The MS:MS_COMMENTS performance of this model was then evaluated using the Set-II and Set-IV samples MS:MS_COMMENTS as holdout test sets. Peak picking Each TIC normalized mass spectrum comprised MS:MS_COMMENTS hundreds to thousands of ion signals within the m/z range of 150-1000. However, MS:MS_COMMENTS most of these signals appeared to have very low intensity and were considered MS:MS_COMMENTS noise signals. Thus, we employed an in-house Python program to remove noises and MS:MS_COMMENTS find the most prominent peaks in the MS dataset, as described below. At first, MS:MS_COMMENTS the entire m/z range (150 to 1000) for each MS data in Set-I was divided into MS:MS_COMMENTS equidistant bins (Δm/z) of width 0.05 m/z. The signal with the highest MS:MS_COMMENTS intensity in each bin was located, and the corresponding m/z values were stored. MS:MS_COMMENTS Then, the most probable position (m/z value) of the most intense signal inside a MS:MS_COMMENTS bin for all MS spectra in Set-I was determined using the following ways: Only MS:MS_COMMENTS those MS samples for a bin were considered for which the maximum intensities MS:MS_COMMENTS were higher than a cutoff value. This cutoff value in a bin was defined as the MS:MS_COMMENTS average value of the median and the mean of the maximum height (intensity value) MS:MS_COMMENTS for that particular bin across all the samples in Set-I. In the next step, the MS:MS_COMMENTS most prominent peak positions were obtained from the distribution of the MS:MS_COMMENTS positions (m/z values) of the maximum intensities in each bin from those MS MS:MS_COMMENTS samples satisfying the abovementioned criteria. However, in some cases, more MS:MS_COMMENTS than one peak was found to be present in a single bin. In that case, the second MS:MS_COMMENTS peak was only considered if the distribution height for the second peak was at MS:MS_COMMENTS least 60% of the most prominent peak. Two peaks found within the 0.02 m/z range MS:MS_COMMENTS were considered to be a single peak, and its position was recalculated as the MS:MS_COMMENTS mean of the peak positions within the 0.02 m/z interval. This resulted in a MS:MS_COMMENTS large number of peak positions (11,000). However, most of the peak positions MS:MS_COMMENTS (m/z) appeared as noises and had very low signal intensities. Those m/z values MS:MS_COMMENTS were removed from the list of prominent peaks following the procedure described MS:MS_COMMENTS below. The maximum intensities for the selected peak positions (11,000) were MS:MS_COMMENTS determined within the ±0.01 m/z for all MS samples in Set-I. A given peak MS:MS_COMMENTS position was considered to be a prominent peak if, at least for five spectra in MS:MS_COMMENTS Set-I, the maximum intensities of that specific peak were higher than 5⨉10-5 MS:MS_COMMENTS (baseline). This constraint reduced the number of prominent peaks to 1753. These MS:MS_COMMENTS 1753 peaks were further examined by plotting all Set-I spectra for each MS:MS_COMMENTS prominent peak, for some of the broad peaks that were assigned more than once in MS:MS_COMMENTS consecutive two or three bins were corrected. The baseline was too high for some MS:MS_COMMENTS peaks to be comparable with the selected peak. Those peaks were excluded, and MS:MS_COMMENTS finally, only 664 peaks were chosen as the most prominent peaks in the Set-I MS:MS_COMMENTS dataset. Feature selection The discriminatory peaks are those features (peaks) MS:MS_COMMENTS that can be used to classify a sample belonging to a particular cohort. Maximum MS:MS_COMMENTS intensities for the selected 664 peaks across all Set-I samples were determined MS:MS_COMMENTS within the ±Δx m/z. As the width of the ion signals increases with the MS:MS_COMMENTS increase of m/z value, Δx was expressed as a function of m/z (x), which MS:MS_COMMENTS linearly increases from 0.005 at 150 m/z to 0.02 at 1000 m/z and written as MS:MS_COMMENTS Δx(x) =0.005 + 0.015 * (x-150)/1000 . The importance score of each of these 664 MS:MS_COMMENTS selected peaks was computed by constructing a binary classification model using MS:MS_COMMENTS the extra trees classifier (ETC) supervised learning algorithm. The MS:MS_COMMENTS ‘ExtraTreeClassifier’ class, as defined in the scikit-learn package, was MS:MS_COMMENTS used in this work. The number of trees in the ETC model was 16, and the maximum MS:MS_COMMENTS depth was 8. The ETC model was trained 20,000 times using randomly selected 80 MS:MS_COMMENTS peaks/features each time. Set-I MS data were used for training and validation MS:MS_COMMENTS purposes of the ETC model. In each iteration, the ETC model was initiated MS:MS_COMMENTS randomly, and each time, the Set-I samples were randomly divided into training MS:MS_COMMENTS and validation sets in a 4:1 ratio. The importance scores of the input features MS:MS_COMMENTS (prominent peaks) were computed and stored only when the model achieved good MS:MS_COMMENTS accuracy in training and validation sets (> 95% for the training set and > 85% MS:MS_COMMENTS for the validation set). The normalized feature importances obtained following MS:MS_COMMENTS the abovementioned procedure are listed for all 664 prominent peaks in Table 3 MS:MS_COMMENTS of the PSIMSresults file. Here, it is worth mentioning that during the training MS:MS_COMMENTS of the models, the input features were standardized using the training data and MS:MS_COMMENTS following the standard procedure: xij = (xij - 𝜇j)/𝜎j, where xij is the MS:MS_COMMENTS value of the j-th feature for the i-th sample, 𝜇j is the mean value of the MS:MS_COMMENTS j-th feature and 𝜎j is the standard deviation of the j-th feature. Machine MS:MS_COMMENTS learning To classify the MS dataset, ML classification models were constructed MS:MS_COMMENTS using the support vector machine (SVM) supervised learning algorithm using the MS:MS_COMMENTS top 60 important signals (based on feature importance). The support vector MS:MS_COMMENTS classification (SVC) method, as implemented in the scikit-learn package, was MS:MS_COMMENTS used in this analysis. It uses the LIBSVM library. The radial basis function MS:MS_COMMENTS (RBF) was chosen as the kernel function for the SVC model. The RBF kernel MS:MS_COMMENTS hyperparameter ‘γ’ (gamma) and regularization parameter ‘C’ were MS:MS_COMMENTS optimized by running several times with different values. Finally, the values MS:MS_COMMENTS for γ and C were set at 0.015 and 10. Set-I data were randomly divided into MS:MS_COMMENTS training and validation sets following a stratified sampling scheme with a 4:1 MS:MS_COMMENTS ratio and used for model training and validation purposes. Set-II (14 males and MS:MS_COMMENTS 14 females) data, i.e., the holdout test set, was used for performance MS:MS_COMMENTS measurement of the optimized ML models. The procedure was repeated 1000 times. MS:MS_COMMENTS The ML model shows the highest prediction accuracy in classifying the MS samples MS:MS_COMMENTS and is considered the best SVM-based ML model. The best SVM model obtained from MS:MS_COMMENTS the abovementioned procedure was subjected to retraining using Set-I and Set-III MS:MS_COMMENTS data based on those top 60 important features to classify the dried blood MS:MS_COMMENTS samples obtained from different solid surfaces (Set-III and Set-IV). Therefore, MS:MS_COMMENTS the new SVM model was optimized using the samples, including Set-I and Set-III. MS:MS_COMMENTS Stratified sampling was used to divide the samples into training and validation MS:MS_COMMENTS sets to avoid class imbalances in the dataset. Thus, in total, 130 samples (90 MS:MS_COMMENTS samples from Set-I and 40 samples from Set-III) were used for training, and 30 MS:MS_COMMENTS samples (22 samples from Set-I and 8 samples from Set-III) were used for MS:MS_COMMENTS validation purposes. Classification results from the best-optimized model are MS:MS_COMMENTS reported in Tables 4-6 of the PSIMSresults file for the training, validation, MS:MS_COMMENTS and test sets, respectively. MS:CAPILLARY_TEMPERATURE 300 degrees Celsius MS:ION_SPRAY_VOLTAGE +5kV MS:MASS_ACCURACY 5ppm MS:MS_RESULTS_FILE ST003654_AN006004_Results.txt UNITS:Total ion current normalized intensity Has m/z:Yes Has RT:No RT units:No RT data #END