lecture 1 INTRO 2023.pdf

Molecular and cellular methods in biomedicine MASTER IN MEDICINE WITH INDUSTRIAL SPECIALIZATION PABLO PENNISI SEPTEMBER 2023 Agenda Outline and methodology of the course The relevance of molecular and cellular approaches in the biomedical sciences Basic concepts on measurement science Presentation o...

Molecular and cellular methods in biomedicine MASTER IN MEDICINE WITH INDUSTRIAL SPECIALIZATION PABLO PENNISI SEPTEMBER 2023 Agenda Outline and methodology of the course The relevance of molecular and cellular approaches in the biomedical sciences Basic concepts on measurement science Presentation of the assignment Discussion (next week) Course methodology Learning elements: – Lectures – Exercises – Preparation (self-study) – Group assignment writing and oral presentation Assessment: – Group assignment (requirement for the final exam) – Final exam (written, no aids allowed) Course methodology Expected scope of the module in terms of ECTS load: Teaching method Confrontation (sessions with teacher) Lectures and problem solving Final assignment workshop Final assignment Preparation for the exam Total Obligatory elements 14 x 4h = 1,87 ECTS 2 x 4h = 0,27 ECTS yes yes Preparation In total (students) (ECTS) 14 x 1,5h = 0,7 ECTS) 2,57 30 h = 1 ECTS 35 h = 1,16 ECTS 1,00 1,16 0,27 5,00 Group assignment Selection of a relevant method and literature research Written report following a “Protocol” template (4-5 pages) Important dates: Online workshop: Oct 17th Deadline for delivery of the written report: Oct 24th Oral presentation: Oct 30th Literature and recommended links Primary textbook (does not cover all subjects) – Principles and Techniques of Biochemistry and Molecular Biology (from 6th edition and over) References supplied by the lecturers Aalborg University Library !! Web sources for the protocol group assignment: – – – – http://www.nature.com/protocolexchange https://experiments.springernature.com http://cshprotocols.cshlp.org http://www.biotechniques.com The use of non-edited web sources (fx. Wikipedia) is strongly discouraged Aim of the course The goal of this module is to introduce students to modern methods used in a biomedical laboratory to investigate and diagnose disease processes, with a focus on methods used to study the diseases at molecular and cellular levels. Nucleic acids Tissue Cells Proteins Example: methods for cancer diagnosis Immunoassays (ELISA, flow cytometry, WB) “Classical” methods Patient’s cells/ tissue sample Histopathology Proteomics Proteins Molecular diagnostics Mass spectrometry Patient’s tissue sample or blood sample Proteomic image Genomics DNA Gene chip Microarray image Advanced diagnostics: Functional genomics Functional genomics is the study of how genes, intergenic regions of the genome, proteins and metabolites work together to produce a particular phenotype. https://www.ebi.ac.uk/training/online/courses/functional-genomics-i-introduction-and-design/what-is-functional-genomics/ Relevance of molecular and cellular methods in biomedicine Drug selection Etiology Disease predisposition Recurrence monitoring Biomarkers Early detection Pre-natal testing The general workflow 1. Problem definition 2. Method selection 3. Measurement, data analysis and interpretation 4. Report 1. Problem definition What is the unknown? What accuracy is required? Is there a time (or money) limit? How much sample is available? What is the concentration range? What components of the sample may cause an interference? How many samples are to be analyzed? Has anyone tried this before? 2. Selection of the technique What type of information is required? (quantitative or qualitative) What type of samples are needed? (tissues, cells, biomolecules) What is the amount of sample needed? What will be the fate of the sample? (destructive or non destructive test) Does the technique provides a functional assessment? Are there safety issues? 3. Measurement, data analysis and interpretation Use of appropriate calibrations and controls Careful record of the results Identification of the variable to be studied and knowledge about other variables that need to be controlled Replication of the measurements Application of appropriate statistical tests (quantitative data) Comparison with existing results/standards Are there specific guidelines? 4. Reporting results Reports must be clear, concise, accurate, and fully interpretative. Use tables/figures for complex datasets When relevant, provide units, scale bars, information about statistical tests, normal ranges. Follow guidelines when available Qualitative assays In qualitative assays the results are not reported as an exact numeric measurement Example: A test whose results are reported as either positive or negative Qualitative ELISA for detection of antibodies in serum Qualitative assays Sensitivity is the ability of the test to detect the target disorder without false positives Specificity is the ability of the method to give a consistent negative result for known negatives Quantitative Analysis Quantitative analysis refers to the process of determining the quantitative concentration of a given component (the analyte) on a biological sample Analytical methods can be characterized by a number of performance indicators: – – – – – – Precision Accuracy Analytical specificity Analytical sensitivity Limit of detection Etc. Quantitative Analysis Precision: refers to the reproducibility of analytical results. When a result is precise, numerical results agree closely. Precision can be estimated by repeating the measurement n times (when possible). Accuracy: describes the correctness of a result by its closeness to an accepted or ‘true’ value. Precise, not accurate Accurate, not precise Accurate and precise Quantitative Analysis Precision is usually expressed either as one standard deviation of the mean or as coefficient of variation of the mean Example: measurements of fasting serum glucose from a patient Measurement (mM) !"#$!%# = 2.42 *+ 2.2 2.3 23 = 0. 165 *+ 2.5 2.5 2.6 ,-#../,/#01 -. "!$/!1/-0 = 23 ∗ 100% = 6.82% !"% Quantitative Analysis Accuracy is usually expressed in terms of confidence intervals, which provide the range of values where there is a given probability that the ‘true’ value lies Example: same measurements as before !"#$""% &' '$""!&( = * − 1 = 4 I&&X HF YFZ!"*F [ % F GHIZ"% '&$ Fℎ" !"%E$"! D&*'E!"*D" E*F"$GHI For 95% confidence interval is t=2.776 D&*'E!"*D" E*F"$GHI = aG# ± = 2.42 ± K∗MN O P.QQR∗S.TRU U = 2.42 ± 0.20 (W Quantitative Analysis Analytical specificity (also called selectivity): the extent to which a method can determine particular analytes under given conditions without interferences from other components. Analytical sensitivity: this is a measure of the change in the response (output) to a defined change in the measured quantity (input) Limits of detection: LLOD (lower) the lowest amount of analyte in the sample, which can be detected but not necessarily quantitated. Also known as minimal detection dose (MDD). ULOD (upper) the highest amount that can be detected before reaching saturation Limits of quantitation: LLOQ (lower) and ULOQ (upper) the lowest and highest amounts of analyte that can be quantitatively determined with suitable precision and accuracy. Quantitative Analysis ULOQ sometimes limit of linearity (LOL) ULOD which quantitative measurements can be made Working range: the range in which the measurements have the same level of accuracy and precision Robustness: a measure of the ability to give consistent results in spite of small changes in experimental conditions such as temperature, pH, etc. Detector response Dynamic range: the range over Analytical sensitivity (slope) LLOQ LLOD Working range Dynamic range Concentration Practical calculations: LLOD= mean of blank + 3 * sd of blank LLOQ= mean of blank + 10 * sd of blank Assignment The assignment can be found in moodle