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MSC III_Advance Forensic Serology_Final.pptx

The Advanced Serology Course is a comprehensive and specialized program designed for professionals in the fields of clinical diagnostics, immunology, and laboratory medicine who seek an in-depth understanding of advanced serological techniques and methodologies. This advanced-level course builds upon foundational knowledge in serology and delves into sophisticated concepts and cutting-edge technologies.

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Advanced Forensic
Serology
Dr. Suchita Rawat Mphil PhD
Objectives
• To gain understanding of the analysis of serological
evidence.
• To conduct statistical examination of serological
evidence.
Course Outcomes
After the successful completion of the course, the student will
be able to:
CO1 : explain the forensic significance of body fluids
CO2 : choose the appropriate serological markers for forensic
examination
CO3 : Assess serological evidence using spectroscopic
methods
CO4 : appraise the techniques of dating biological stains
CO5 : summarize the molecular techniques used in forensic
serology
Unit /Topic No. OF
HOURS
TEACHING METHODOLOGY TIME OF
COMPLETION
Unit 1: Serological Analysis of Body Fluids 12 Mapped to Ms. Aditi Mishra
Unit 2: Spectroscopic Analysis of Body Fluids
Spectroscopy techniques for the forensic identification of body
fluids: Ultraviolet-visible Spectroscopy, Infrared Spectroscopy,
Raman Spectroscopy, X-ray fluorescence, Nuclear Magnetic
Resonance, and Mass Spectrometry; Interpretation of results and
appraisal of limitations.
11 Participatory TL : Interactive Lecture,
Technical presentation
Problem Solving TL: Subject related
Exercises, Content Analysis
Sep 4- Sep 9, 2023
Unit 3: Molecular Approaches for Forensic
Serology
12 Mapped to Ms. Aditi Mishra
Unit 4: Time Since the Deposition of Biological
Stains
Time since the deposition of biological stains; color
analysis and ultraviolet-visible spectroscopy,
measurement of fluorescence lifetime, Raman and
near-infrared spectroscopy; Time since the deposition
of stain using microbial markers; Interpretation of
results and appraisal of limitations.
12 Participatory TL : Interactive Lecture,
Technical presentation
Problem Solving TL: Subject related
Exercises, Content Analysis
Oct 30 – Oct 31, 2023
Unit 5: Statistical Interpretation of Serological
Markers
13 Mapped to Ms. Aditi Mishra
Seminar & Activity
Activity based (5 marks): Mapped to Ms. Aditi
Seminar Presentation (10 Marks): Students would have a present case study on dating of
biological stain or present research article using analytical techniques/ microbial marker for time
since deposition
Assessment criteria (5 marks)
Presentation (2)
Content (2)
Reference (1)
Skill Development Activity
• Problem Solving TL: Case study
• Experiential TL: Article Review
Spectroscopic analysis of body fluids: UV
and IR
Locard's principle, "every contact leaves a trace,"
BODY FLUIDS
Ad

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MSC III_Advance Forensic Serology_Final.pptx

  • 2. Objectives • To gain understanding of the analysis of serological evidence. • To conduct statistical examination of serological evidence. Course Outcomes After the successful completion of the course, the student will be able to: CO1 : explain the forensic significance of body fluids CO2 : choose the appropriate serological markers for forensic examination CO3 : Assess serological evidence using spectroscopic methods CO4 : appraise the techniques of dating biological stains CO5 : summarize the molecular techniques used in forensic serology
  • 3. Unit /Topic No. OF HOURS TEACHING METHODOLOGY TIME OF COMPLETION Unit 1: Serological Analysis of Body Fluids 12 Mapped to Ms. Aditi Mishra Unit 2: Spectroscopic Analysis of Body Fluids Spectroscopy techniques for the forensic identification of body fluids: Ultraviolet-visible Spectroscopy, Infrared Spectroscopy, Raman Spectroscopy, X-ray fluorescence, Nuclear Magnetic Resonance, and Mass Spectrometry; Interpretation of results and appraisal of limitations. 11 Participatory TL : Interactive Lecture, Technical presentation Problem Solving TL: Subject related Exercises, Content Analysis Sep 4- Sep 9, 2023 Unit 3: Molecular Approaches for Forensic Serology 12 Mapped to Ms. Aditi Mishra Unit 4: Time Since the Deposition of Biological Stains Time since the deposition of biological stains; color analysis and ultraviolet-visible spectroscopy, measurement of fluorescence lifetime, Raman and near-infrared spectroscopy; Time since the deposition of stain using microbial markers; Interpretation of results and appraisal of limitations. 12 Participatory TL : Interactive Lecture, Technical presentation Problem Solving TL: Subject related Exercises, Content Analysis Oct 30 – Oct 31, 2023 Unit 5: Statistical Interpretation of Serological Markers 13 Mapped to Ms. Aditi Mishra
  • 4. Seminar & Activity Activity based (5 marks): Mapped to Ms. Aditi Seminar Presentation (10 Marks): Students would have a present case study on dating of biological stain or present research article using analytical techniques/ microbial marker for time since deposition Assessment criteria (5 marks) Presentation (2) Content (2) Reference (1)
  • 5. Skill Development Activity • Problem Solving TL: Case study • Experiential TL: Article Review
  • 6. Spectroscopic analysis of body fluids: UV and IR Locard's principle, "every contact leaves a trace," BODY FLUIDS
  • 7. Reference: Zapata, F., de la Ossa, M. Á. F., & García-Ruiz, C. (2015). Emerging spectrometric techniques for the forensic analysis of body fluids. TrAC Trends in Analytical Chemistry, 64, 53-63.
  • 8. Reference: Zapata, F., de la Ossa, M. Á. F., & García-Ruiz, C. (2015). Emerging spectrometric techniques for the forensic analysis of body fluids. TrAC Trends in Analytical Chemistry, 64, 53-63.
  • 9. Reference: Zapata, F., de la Ossa, M. Á. F., & García-Ruiz, C. (2015). Emerging spectrometric techniques for the forensic analysis of body fluids. TrAC Trends in Analytical Chemistry, 64, 53-63.
  • 10. Reference: Zapata, F., de la Ossa, M. Á. F., & García-Ruiz, C. (2015). Emerging spectrometric techniques for the forensic analysis of body fluids. TrAC Trends in Analytical Chemistry, 64, 53-63.
  • 11. Reference: Zapata, F., de la Ossa, M. Á. F., & García-Ruiz, C. (2015). Emerging spectrometric techniques for the forensic analysis of body fluids. TrAC Trends in Analytical Chemistry, 64, 53-63.
  • 12. • Disadvantages OF PRESUMPTIVE AND CONFIRMATORY TESTS , such as: • (1) most confirmatory tests (for blood and semen) are destructive • 2) it is necessary to apply different tests to confirm each type of body fluid; this limitation requires division of a sample into several parts, and a portion of the sample having to be kept for possible future analyses. Reference: Zapata, F., de la Ossa, M. Á. F., & García-Ruiz, C. (2015). Emerging spectrometric techniques for the forensic analysis of body fluids. TrAC Trends in Analytical Chemistry, 64, 53-63.
  • 13. Spectroscopic analysis of body fluids: UV and IR
  • 14. UV-Vis radiation may undergo, absorption or fluorescence emission.
  • 15. most body fluids (e.g., semen, saliva, and urine) blood Reference: Zapata, F., de la Ossa, M. Á. F., & García-Ruiz, C. (2015). Emerging spectrometric techniques for the forensic analysis of body fluids. TrAC Trends in Analytical Chemistry, 64, 53-63.
  • 16. Discrimination of Body Fluids using ATR-FTIR
  • 19. This Photo by Unknown Author is licensed under CC BY
  • 20. Reference: Aparna, R., Iyer, R. S., Das, T., Sharma, K., Sharma, A., & Srivastava, A. (2022). Detection, discrimination and aging of human tears stains using ATR-FTIR spectroscopy for forensic purposes. Forensic Science International: Reports, 6, 100290.
  • 21. Reference: Aparna, R., Iyer, R. S., Das, T., Sharma, K., Sharma, A., & Srivastava, A. (2022). Detection, discrimination and aging of human tears stains using ATR-FTIR spectroscopy for forensic purposes. Forensic Science International: Reports, 6, 100290.
  • 22. Reference: Aparna, R., Iyer, R. S., Das, T., Sharma, K., Sharma, A., & Srivastava, A. (2022). Detection, discrimination and aging of human tears stains using ATR-FTIR spectroscopy for forensic purposes. Forensic Science International: Reports, 6, 100290.
  • 23. Reference: Aparna, R., Iyer, R. S., Das, T., Sharma, K., Sharma, A., & Srivastava, A. (2022). Detection, discrimination and aging of human tears stains using ATR-FTIR spectroscopy for forensic purposes. Forensic Science International: Reports, 6, 100290.
  • 24. Reference: Aparna, R., Iyer, R. S., Das, T., Sharma, K., Sharma, A., & Srivastava, A. (2022). Detection, discrimination and aging of human tears stains using ATR-FTIR spectroscopy for forensic purposes. Forensic Science International: Reports, 6, 100290.
  • 25. Reference: Aparna, R., Iyer, R. S., Das, T., Sharma, K., Sharma, A., & Srivastava, A. (2022). Detection, discrimination and aging of human tears stains using ATR-FTIR spectroscopy for forensic purposes. Forensic Science International: Reports, 6, 100290.
  • 27. Materials and method Sample selection Animal blood In the present study, animal blood specimens were collected from the repository of wildlife institute of India, Dehradun. The blood samples were acquired from four species, namely Asian Elephant, Indian Leopard, Royal Bengal Tiger, and Domestic pig. Details of collected samples are enumerated in Table 1. 2.1.2. Human blood The Human blood samples (n = 14) collected from superficial vein was employed to test the validation method. Sample preparation The blood samples were prepared by placing an aliquot of 10–20 μl of blood of each selected species on clean and sterile glass slide and each sample was allowed to dry completely for further analysis. Then, using sterile spatula, dried sample was scraped out and homogeneously deposited on the surface of ATR crystal and directly analyzed for spectral acquisition. For each selected animal blood species, three replicate spectra were acquired from separate aliquots to check the reproducibility.
  • 28. Instrumentation For the analysis, the ZnSe (Zinc selenide ) ATR crystal face was properly cleaned with acetone wipes. The background scan was executed without placing any sample on the surface of ATR crystal for the spectral acquisition. The eco- ATR FT-IR spectrometer (Bruker Alpha) enclosed with Smart Orbit, ZnSe crystal face with OPUS (v 8.0) software was utilized for the scanning of blood samples in the MIR spectral range that is 600–4000 cm-1. Samples were scanned with accumulations of 24 scans at 4 cm-1 resolution.
  • 29. Reference:Sharma, C. P., Sharma, S., & Singh, R. (2023). Species discrimination from blood traces using ATR FT-IR spectroscopy and chemometrics: Application in wildlife forensics. Forensic Science International: Animals and Environments, 3, 100060 The region of amide I (1643 cm-1), amide II (1527 cm1 ), amide III (1229–1301 cm-1), and fibrinogen, haptoglobin, IgA, IgG, and IgM (1093 cm-1) are the most important variables for the discrimination of all selected species from blood
  • 30. Reference: Sharma, C. P., Sharma, S., & Singh, R. (2023). Species discrimination from blood traces using ATR FT-IR spectroscopy and chemometrics: Application in wildlife forensics. Forensic Science International: Animals and Environments, 3, 100060
  • 31. Reference: Sharma, C. P., Sharma, S., & Singh, R. (2023). Species discrimination from blood traces using ATR FT-IR spectroscopy and chemometrics: Application in wildlife forensics. Forensic Science International: Animals and Environments, 3, 100060
  • 33. Reference: Muro, C. K., Doty, K. C., de Souza Fernandes, L., & Lednev, I. K. (2016). Forensic body fluid identification and differentiation by Raman spectroscopy. Forensic Chemistry, 1, 31-38. 2. Materials and methods 2.1. Sample analysis A total of 75 samples were purchased from Bioreclamation IVT, Inc. (Westbury, NY) and Lee Biosolutions, Inc. (Maryland Heights, MO). The sample population included peripheral blood, saliva, semen, sweat, and vaginal fluid donors (n = 15 each). Peripheral blood samples were prepared in 30 microL aliquots, while saliva, semen, sweat, and vaginal fluid were all prepared with only 10 microL. Samples were deposited onto individual microscope slides, which had been covered with aluminum foil to avoid fluorescence interference [37], and allowed to dry completely. The dried traces were approximately 15 mm2 in area. Spectra were collected using an inVia Raman spectrometer (Renishaw, Inc., Hoffman Estates, IL) operated with WiRE 3.2 software. All samples were excited with a 785 nm wavelength laser.
  • 34. Reference: Muro, C. K., Doty, K. C., de Souza Fernandes, L., & Lednev, I. K. (2016). Forensic body fluid identification and differentiation by Raman spectroscopy. Forensic Chemistry, 1, 31-38. Body Fluid Raman spectra Blood  Strong peaks: at 753, 1372, 1577, and 1620 cm-1: porphyrin and pyrrole rings of the heme groups appear  Broad peak at 1245 cm-1 from Amide III  Sharp peak at 1002 cm-1 from phenylalanine Saliva (mostly due to amino acids)  weak peak at 853 cm-1 from Tyrosine  strong peaks at 1003 and 1444 cm- 1, from phenylalanine and tryptophan, respectively .  Wide peak at 1655 cm-1 from Amide I
  • 35. Reference: Muro, C. K., Doty, K. C., de Souza Fernandes, L., & Lednev, I. K. (2016). Forensic body fluid identification and differentiation by Raman spectroscopy. Forensic Chemistry, 1, 31-38. Body Fluid Raman spectra Semen  strong peak at 715 cm-1 is due to C–N symmetric stretching in choline .  strong peak at 830 cm-1 from Fructose and tyrosine  strong peak at 850 cm-1 from Tyrosine, lactate and tryptophan  peak at 958 cm-1 from citric acid and spermine phosphate hexahydrate  Sharp peak at 1003 cm-1 due the aromatic ring breathing of phenylalanine and the C–N stretching of urea .
  • 36. Reference: Muro, C. K., Doty, K. C., de Souza Fernandes, L., & Lednev, I. K. (2016). Forensic body fluid identification and differentiation by Raman spectroscopy. Forensic Chemistry, 1, 31-38. Body Fluid Raman spectra Sweat  Peak due to lactate (856, 926, 1044, and 1452 cm-1)  Peak due urea (1003 cm-1) . Vaginal scretions  Peak from lactate at 853, 1126, 1450, and 1653 cm-1.  Peak fromTryptophan at 1450 and 1339 cm-1.  Urea peak at 541, 1003, and 1653 cm-1 .  Phenylalanine peak at 1003 cm-1
  • 38. Material and methods 2.1. Samples Human body fluids were purchased from Bioreclamation, Inc. (semen – 9 donors, East Meadow, NY) and Lee Biosolutions, Inc. (vaginal fluid – 7 donors, East St. Louis, MO). Raman spectra of pure body fluids and their mixtures were acquired from samples dried overnight at room temperature under ambient conditions. Mixtures of vaginal fluid and semen were prepared using random pairs in ratios of 1.5:98.5, 3:97, 7:93, 12.5:87.5, 25:75, 50:50, 75:25, 93:7, and 97:3% and mixed thoroughly for 30 s using a vortex. One drop (~10 µL) was placed on a standard microscope slide covered with aluminum foil. Aluminum foil was chosen to minimize Raman and fluorescent interference from the substrate. 2.2. Instrumentation Raman spectra were acquired using a Renishaw inVia Raman spectrometer coupled with a research-grade Leica microscope with a 50x objective. A 785-nm laser beam used for excitation was focused on a ~ 10-µm spot on a dry body fluid sample. A ~ 3.5x2.5 mm sample area was scanned using Renishaw PRIOR automatic stage. Raman spectra were recorded with a 10-second accumulation time from 80 equally spaced points on the sample. The instrument calibration was done using the Raman band of silicon standard at 520 cm− 1 .
  • 39. Reference: Sikirzhytskaya, A., Sikirzhytski, V., Pérez-Almodóvar, L., & Lednev, I. K. (2023). Raman spectroscopy for the identification of body fluid traces: Semen and vaginal fluid mixture. Forensic Chemistry, 32, 100468.
  • 40. Biological fluid Raman spectra Vaginal fluid 1002 cm-1 phenylalanine, urea 1045 cm-1 , 1655 cm-1 lactic acid, proteins, urea 1082, 1610 cm-1 cm-1 lactic acid 1310–1337 cm-1 Proteins 1445–1455 cm-1 lactic acid, urea Semen 829, 983, 1616 cm-1 tyrosine 888, 958 , 1055, 1125, 1317, 1461 cm-1 Sphingomyelin (SPH) 1003 cm-1 Albumin, phenylalanine 1065 cm-1 Sphingomyelin (SPH), fatty acid 1327 cm-1Tyrosine, CH3CH2 wagging mode in purine bases of nucleic acids 1448 cm-1 Albumin, cholin Most contributing Raman bands from the viewpoint of classification Reference: Sikirzhytskaya, A., Sikirzhytski, V., Pérez-Almodóvar, L., & Lednev, I. K. (2023). Raman spectroscopy for the identification of body fluid traces: Semen and vaginal fluid mixture. Forensic Chemistry, 32, 100468.
  • 42. Sample preparation and Raman microspectroscopy Blood and semen samples were purchased from several companies including Bioreclamation, Inc., Lee Biosolutions, Inc., and Biological Specialty Corp. Blood/ semen stains were prepared using samples from two different anonymous individuals (a Caucasian male for semen and a Caucasian female for blood). Both donors were found to be negative for HbsAg, HCV, HIV-1&2, syphilis and HIV-1 antigen. All samples, in volumes of 10 mL each, were placed on microscope slides that were covered with aluminum foil to reduce fluorescence. Mixtures were prepared with different blood/semen ratios (5:95, 10:90, 20:80, 30:70, 40:60, 50:50, 70:30, 75:25, 85:15, 85.5:12.5, 92.75:6.25, 96.875:3.125 and 98.437:1.5625) by thoroughly shaking for 20 s. All samples were allowed to dry completely overnight.
  • 43. Reference: Sikirzhytski, V., Sikirzhytskaya, A., & Lednev, I. K. (2012). Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: semen and blood mixtures. Forensic science international, 222(1-3), 259-265. Distinctive Raman bands of blood (754, 1003, 1226, and 1619 cm-1 ) and semen (716, 830, 959, 1268, 1329, and 1671 cm-1 ) can be used to identify their contributions in mixtures.
  • 45. Materials and methods Materials Whole blood samples from a cow (n = 3), horse (n = 3), sheep (n = 3), pig (n = 3), rabbit (n = 3), and chicken (n = 3) were obtained from Cosmo Bio Co., Ltd. (Tokyo, Japan). Human blood samples (n = 5) were taken intravenously from the authors. Whole blood samples of cat (n = 1) and dog (n = 2) were kindly provided by a veterinary clinic (Ekinan Animal Hospital). Whole blood samples (n = 3) of rat and mouse were kindly provided by the Department of Experimental Animals, Interdisciplinary Center for Science Research, Organization for Research, Shimane University. About 100 μL of the blood was dropped onto gauze to make a bloodstain. After drying, all Raman measurements were performed within 48 h of sample (The spectra range was 100– 2000 cm−1 using He-Cd laser light (532 nm) was used for excitation.) For the time course change study, bloodstains from a human, rat, mouse, cow, pig, and rabbit were left at room temperature for 3 months (1 day, 1 week, 2 weeks, 1 month, 2 months, and 3 months). The maximum sample dilution to detect Raman spectroscopy was investigated by diluting the human blood (1:10, 1:50, 1:100, and 1:250) with saline.
  • 46. McLaughlin, G., Doty, K. C., & Lednev, I. K. (2014). Discrimination of human and animal blood traces via Raman spectroscopy. Forensic science international, 238, 91-95. Raman peaks for blood (742, 1001, 1123, 1247, 1341, 1368, 1446, 1576, and 1619 cm−1 ) could be observed using a portable Raman spectrometer, and human bloodstains could be distinguished from nonhuman ones by using a principal component analysis.
  • 51. Spectroscopic Analysis of Body Fluids using NMR
  • 53. Sample collection A total of 21 healthy subjects were recruited for this study, 13 male and 8 female individuals. The mean age of the volunteers was 35± 10 years (range 28–51 years) and the body weight between 50 and 105 kg; 7 subjects were non-smokers while the remaining were smokers. Semen samples were collected by masturbation after 3 days of abstinence. Unstimulated whole saliva samples were collected randomly during the day. (5–6 ml) Urine samples were collected randomly during the day in a urine sterile box. Venous blood was collected in sterile plastic vials. All the collected BFs (saliva, semen, blood, and urine) were centrifuged at 13 000 rpm and 4 C for 10 min in order to separate the serum from the blood, separate the seminal fluid from the semen, and remove large proteins, cells, and solid debris from the urine and saliva samples. Approximately 1 ml of the supernatant of all samples was dried using a Speed Vacuum Concentrator (Eppendorf) and immediately stored at –80 C. Furthermore, aliquots of venous blood were disposed in a permeable support (tissue) and were allowed to coagulate; the spot was then eluted using 2 ml 0.9% NaCl solution overnight at 37 C and then stored at –80c Before NMR analysis, frozen samples were reconstituted in 660 ml of D2O with 0.77 mM of TSP as internal standard and then transferred into 5-mm NMR tube
  • 54. Figure 2.Typical 1 H NMR spectra of (A) saliva, (B) seminal fluid, (C) serum, and (D) urine samples in D2O. Main assignments are reported. Isoleucine (Ile), leucine (Leu), valine (Val), fatty acids (FA), propionate (Prop), lactate (Lac), alanine (Ala), acetate (Ace), N- acetyl (NAc) groups, citrate (Cit), creatine (Crt), creatinine (Crn), choline (Cho), glycerophosphorylcholine (GPC), trimethylamine N-oxide (TMAO), glucose (Glu), caffeine (Caf), sucrose (Suc), hippurate (Hip), uridine (Uri), tyrosine (Tyr), histidine (His), phenylalanine (Phe), and formate (Form).
  • 55. Body Fluids Metabolites (ppm) Saliva Propionate (1.06, 2.18), lactate (1.34, 4.13), acetate (1.92), N-acetyl groups (2.06), tyrosine (6.90, 7.20), formate (8.46) Semen Leucine/Isoleucine/Valine (0.90–1.08), lactate (1.34, 4.13), citrate (2.61), choline (3.21), GPC (3.23), tyrosine (6.86, 7.17), uridine (5.88, 5.92, 7.82), phenylalanine (7.30–7.44), histidine (7.06, 7.78)
  • 56. Body Fluids Metabolites (ppm) Serum Fatty acids –CH3 (0.90) and –CH2 (1.30), lactate (1.34), N-acetyl groups (2.06), glucose (3.35–3.60, 5.25), fatty acids – CH=CH (5.32), formate (8.46) Urine Lactate (1.34), alanine (1.49), citrate (2.67),TMAO (3.34), creatine (3.07, 3.86), creatinine (3.07, 4.13), hippurate (3.97, 7.54, 7.63, 7.83), formate (8.46)
  • 57. PCA Spectra: saliva (blue diamond), seminal fluid (red box), serum (yellow inverted triangle), urine (green triangle), mixtures (light blue circle)
  • 59. Spectroscopic Analysis of Body Fluids using Mass spectroscopy
  • 61. FIG. 1. Recent workflow improvements and the reference channel for body fluid proteomics. A, Recent workflow improvements. Removal of highly abundant proteins, e.g. albumin in plasma, alleviates the high dynamic range challenge of body fluids. Automation enables robust and high-throughput preparation of samples to obtain clean peptides from proteins. For liquid chromatography separation of peptides, short and high inner diameter columns synergize with robust and fast measurements. MS measurement is aided by additional information such as ion mobility or rapidly moving quadrupole windows
  • 62. . B, Multiplexed reference channel. Non- isobaric labeling—here by dimethyl reagents— creates channels recognizable by MS1, permitting the pooling of samples to be measured and the addition of a reference proteome. Channels are related to each other by their characteristic mass shifts. The identifications in the reference proteome in each MS measurement carry over to the sample channels, removing the need for their identification. Additionally, the reference channel enables real-time monitoring of analytical performance. Being identical in all samples, it serves as a bridge for quantitative comparisons across samples of a study or even between studies and laboratories. Different reference proteomes can also be “translated” to each other or ‘harmonized’ by measuring them together. For absolute quantification, a subset of proteins in a reference proteome can be related to absolute standards.
  • 63. The state of MS-based body fluid proteomics. A, Workflow capabilities of MS proteomics. Identification: MS can conceptionally identify any protein using specific mass patterns, a process that is reliable and can be controlled in terms of false discovery rates (FDR) independent of a body fluid matrix. In practice, the high range of protein abundances in body fluids is a challenge, limiting identifications of very low abundant proteins. Quantification: Accuracy, precisions, and dynamic range of quantitative measurement are the core strength of MS-based proteomics. In practice, the same caveats apply to low abundance proteins, an area that is being addressed by current technological developments. Relative comparisons within and across samples are possible for the entire proteome, while the determination of absolute levels is limited to a few proteins. Process: It is now feasible to measure thousands of samples but the measurement should be tightly controlled for stable calibration, need for cleaning or column replacement. Proteomes inherently contain information to assess samples and processing quality, which can be monitored continuously. Comparing results between laboratories should result in overlapping biomarker candidates, however, a reference proteome (see Fig. 1) would greatly improve this.
  • 64. B, Body fluid proteome information produced by MS-based proteomics. Proteins can be precisely identified and quantified, whereas the resolution of splicing isoforms and single amino acid isoforms is also possible in principle but not widely established yet. Likewise, investigation of endogenous peptides is technically feasible but rarely performed on a large scale in body fluids, which also applies to post-translational modifications. Protein structure can be studied on a proteome scale but this is even further off into the future and only realistic for the most abundant proteins in body fluid
  • 68. Fig. 2. (a)Typical MALDI-TOF MS protein profiles of volunteer body fluids mixture (1:50 blood + raw semen + raw saliva).The mixtures were spotted with equal volume of SA matrix and the data acquired in linear positive mode
  • 69. . (b) MALDI-MS/MS spectrum of precursor ion m/z 1355.64 of a protein fragment of semenogelin 2 (semen). (c) MS/MS spectrum of precursor ion m/z 1538.44 of a protein fragment of alpha amylase 1 (saliva) and matched “b” and “y” ions are indicated in the spectra. The 1 L of body fluid samples were directly mixed with an equal volume of MALDI matrix (-CHCA 10 mg/mL in 50% LC–MS grade ACN/H2O) and mixture was spotted on MTP polished steel plate.
  • 70. Fig. 3. (a) MALDI-TOF mass spectrum of human urine. 1 L of urine samples were directly spotted with -CHCA matrix on MTP target and the endogenous peptide (m/z 1912.12) of uromodulin was identified using database analysis (Swiss-Prot). Inset: a mass spectrum showing isotopic pattern of the peptide. (b) MALDI-MS/MS data of urine peptide m/z 1912.12 and matched ion fragment masses are identified on X-axis, intensities onY-axis.
  • 71. Fig. 4. MALDI-TOF mass spectra of an 11-year-old proficiency test sample from the “victim’s” shirt. (a) Intact protein mass matched to human - and -hemoglobin proteins, inset spectrum indicates the presence of heme group (m/z 616.2). (b)Tryptic peptides (PMF),
  • 72. MALDI-TOF MS sample preparation
  • 73. Time since deposition (TsD) • From a criminalistic point of view • estimation of when the crime was committed would be useful to determine the relevance of trace samples found at the scene • enable the verification of witnesses’ statements • limit the number of suspects • help corroborate alibis
  • 74. techniques forTsD determination spectroscopy, chromatography, and electron spin resonance (coloured stains and not colourless) degradation patterns of different human messenger RNA (mRNA) and ribosomal RNA (rRNA) transcripts ß-actin and 18S-rRNA (blood) microbial forensics
  • 81. Material and methods • Body fluid samples • Forensically relevant body fluids (blood, menstrual blood, saliva, semen, vaginal secretion) were collected on sterile cotton swabs (Milian, Nesselnbach, Switzerland) • Menstrual blood and vaginal secretion were self-collected from the vagina directly on swabs by the donors. • Semen and saliva were self-collected in sterile tubes, and 50 µl each were spotted on the swabs in the laboratory. • Blood was collected by venipuncture into EDTA coated tubes and 50 µl were directly pipetted onto the swabs (EDTA EFFECTCONTROLLED) • Three biological replicates were collected for each body fluid (i.e. from three individuals). • In total 12 different donors participated in the study, each providing a sample from only one body site, with the exception of three participants.
  • 82. RNA EXTRACTION • For blood, saliva and semen samples, entire swabs were utilized for the RNA extraction. • For vaginal secretion and menstrual blood samples, only ½ of the cotton tip of a swab was removed at a given sampling time point and used for RNA extraction
  • 83. Environmental condition • (1) indoors, at room temperature in a dark and dry place (in a cupboard), • (2) outdoors on the flat rooftop of the institute building (exposed to sun and wind but protected from rain). • The samples were put in place in March–April for up to 1.5 years. • RNA was extracted immediately after sample collection (time point 0/deposition) and after 1 day, 7 days, 4 weeks, 6 months, 1 year and 1.5 years. • This sampling scheme resulted in a total of 210 samples (5x3x7x2): for each of the 5 body fluids, we had 3 different donors, 7 time points, and 2 environmental conditions. • Negative controls (1+2), comprising swabs without any sampled fluid, were also included: one sample processed right away without aging, and another two exposed for 4 weeks, in either indoor or outdoor conditions.
  • 84. WORKFLOW RNA was extracted using the ReliaPrep™ RNA Cell Miniprep kit (Promega, Dübendorf, Switzerland). RNA quantity was assessed using the QuantiFluor® RNA HS System (Promega) Total RNA libraries were constructed using theTrio RNA- Seq kit (Nugen, Leek, The Netherlands) Libraries were quality checked on the TapeStation 4200 (AgilentTechnologies, Santa Clara, USA). Libraries generated from fresh, 1 day, 7 days and 4 week-old stains were sequenced on the Illumina HiSeq 4000 platform, while 6 months, 1 and 1.5 year old samples were sequenced on the Illumina NovaSeq 6000 platform.
  • 85. Taxonomic profiles • Taxonomic assignment of the raw reads was conducted using kraken2 [55]. We used a customized database containing not only the genomes that are in the standard reference database (bacteria, archaea, viruses, human, Univec) but also the genomes of fungi, protozoa and plants
  • 86. . In outdoor samples, we observed a consistent compositional shift, occurring after 4 weeks: this shift was characterized by an overall increase in non-human eukaryotic RNA and an overall decrease in prokaryotic RNA
  • 87. . In depth analyses showed a high fraction of tree, grass and fungal signatures, which are characteristic for the environment the samples were exposed to.
  • 88. When examining the prokaryotic fraction in more detail, three bacterial phyla were found to exhibit the largest changes in abundance, namely Actinobacteria, Proteobacteria and Firmicutes. 26 bacterial orders to be indicative of sample age
  • 89. Indoor samples did not reveal such a cle compositional change at the domain level: eukaryotic and prokaryotic abundance remained relatively stable across the assessed time period. Nonetheless, a Lasso regression analysi identified 32 bacterial orders exhibiting clear changes over time, enabling the prediction ofT
  • 90. TsD are part of the Actinobacteria, Proteobacteria and Firmicutes.
  • 91. we found that the observed changes across time are not primarily due to changes associatedwith body fluid specific bacteria but mostly due to accumulation of bacteria from the environment.
  • 93. Blood traces are one of the most frequently found body fluids in cases of homicides, battery, and sexual assault . However, blood present at a scene may not always be relevant to the crime that occurred i.e. An individual may have cut themselves shaving or while cooking and did not properly clean up. Therefore, a major concern in criminal investigations is being able to distinguish between bloodstains that are relevant to the crime versus those that are extraneous.
  • 94. Fundamentals of Blood Aging proteins, nucleic acids, lipid, and carbohydrate exist at low concentrations in the plasma
  • 95. The iron ion forms bonds with the four nitrogen atoms that are present in the heme compound and a fifth bond with a segment of the Hb chain ,The sixth bond that occurs with this ferrous ion is usually with O2
  • 97. When all the oxyHb has degraded to hemichrome, presumptive blood tests that rely on the presence of oxyHb no longer work The conversion process of oxyHb to metHb to hemichrome has commonly been used as a method to determine the TSD of bloodstains.
  • 98. color chart Solubility (fresh bloodstains: rapidly dissolve / spectrum of pure oxyHb), bloodstains aged more water- insoluble/ spectrum of metHb HPLC (β-chains to γ-chains as a measure ofTSD) X PEAK Atomic force microscopy (physical changes r to the surface of red blood cells) Electron paramagnetic resonance spectroscopy (the spin state of the iron ion in the hemE) Techniques for the analysis of red blood cells and hemoglobin
  • 99. . Ultraviolet-visible (UV-Vis) absorption spectroscopy changes to the Hb Soret band, which has a max absorbance at 412 nm. They discovered that this band underwent a blue spectral shift, or a shift to a lower wavelength, as the bloodstain aged. However, this technique is temperature dependent. The blue shift of the Soret band occurred faster and had a longer shift in bloodstains that were exposED to heated conditions.
  • 102. new bands at 667, 747, and 1248 cm-1 markers of Fe-O2 at bands at 419, 570 and 1638 cm-1 disappeared 377 cm−1 band (metHb) increased its intensity in relation to 420 cm−1 (oxyHb marker); intensity of band at 1252 cm−1 (part of the amide III spectral region, assigned to random coil) increased , intensity of bands at 637 (O2 marker band) and 1224 cm−1 (part of the amide III spectral region, assigned to β-sheet) decreased;
  • 103. decrease of the 1224 cm-1 band and increase of the 1252 cm-1 band, corresponding to transformation from β- sheet into random coil (continue up to one month since bloodstain deposition) increase of the 970 cm-1 band’s intensity, reflecting Hb aggregation red shift of the band a520 cm -1 to 500 cm -1 (after one month elapsed since deposition) with subsequent increase of its intensity, red shift of 676 cm-1 and 754 cm-1 bands to 660 cm - 1 and 746 cm-1
  • 104. diminishing of following bands over time (non-existent in 1-year-old sample’s spectrum): 345, 419, 1562, 1600, 1619, 1637 and 1653 cm-1 increase of the intensity of 440 cm-1 (novel spectral feature, non-existing in spectra fresh blood)
  • 106. ATR FT-IR spectra of aged BF samples stored at ambient conditions for one day, one week, one month, three months, fve months or eight months  decreases in the characteristic bands of proteins, Amide I and Amide II were identified in the spectra of aged blood, saliva and semen, indicating degradation of the protein structures.  The spectra of aged urine and sweat showed possible influences from differences in humidity, or water content, in the 3500-3100 cm−1 region.  However, significant decreases in the signals at approximately 1590 cm−1 and 1460 cm−1 in the aged urine spectra and approximately 1455 cm−1 in the aged sweat spectra were indicative of the decomposition of urea.
  • 108. The decrease in peak intensity at 1 640 cm-1 and 1 539 cm- 1 could be due to the degradation of proteins and other macromolecules The increase in peak intensity at 1392 cm1 (representing COO stretching) may be caused by break of peptide chain and increment of free amino acids. The PO2 peaks at 1232 cm1 , the increase in peak intensity may be related to the degradation and rupture of sperm cells.

Editor's Notes

  1. This is the question that your experiment answers
  2. Summarize your research in three to five points.
  3. Americium
  4. Trimethylsilylpropanoic acid (TMSP or TSP)
  5. Heme is composed of a cyclic ring called porphyrin which is made up of four pyrrole entities that are linked by a methylene bridge, forming a unit called protoporphyrin IX. When this moiety is bound with an iron in its ferrous form (Fe2+) it is called ferroprotoporphyrin.