in this lesson
Module Overview
Topic: Immunology
Audience: Advanced undergraduate students, introductory graduate students
Resource type: Full module, CREATE Method
Module outline: 12 lessons over ~ 4 weeks
Primary literature analyzed: Persistent serum protein signatures define an inflammatory subcategory of long COVID by Talla et al. (2023)
CREATE Method:
This 4-week module uses the CREATE method (Hoskins et al., 2007) to walk students through a scaffolded approach to reading Persistent serum protein signatures define an inflammatory subcategory of long COVID by Talla et al. (2023). Rather than read the paper in its entirety all at once, students walk through the paper section-by-section, completing worksheets and discussing with their peers along the way.
Throughout the module, students analyze the following methods:
- Olink proteomics for the detection of 1500+ proteins in participant serum samples
- Proximity Extension Analysis (PEA)
- Next Generation Sequencing (NGS)
- ELISAs
- K-means clustering
- Validation of results with an independent open dataset
More Information
Module Timeline
This module consists of 12 lessons. We encourage you to remix and customize these lessons to fit the needs of your course. See the instructor guide for more details about alternative ways to teach this module, including abridged versions. Several lessons contain worksheets for students to walk through different sections of the paper.
Data Analysis Sheets (DAS): For each figure in the paper, students are asked to complete a data analysis sheet. These worksheets ask students specific questions about the figure and their interpretation of the data. Some questions featured within DAS worksheets include:
- What is your simplified, decoded, in regular language title for this figure?
- What is the specific hypothesis being tested, or specific question being asked in the experiment represented in panel A?
- Do you think that the data in panel B support a difference in RBD IgG between the groups? Provide evidence for why or why not.
Methods Worksheets: This module contains two methods worksheets where students are asked in-depth questions about the methods used in the paper. Some examples include:They measured analytes from inflammation, oncology, cardiometabolic, and neurology panels. What is a “panel” and how do these different panels differ from one another?
- They measured analytes from inflammation, oncology, cardiometabolic, and neurology panels. What is a “panel” and how do these different panels differ from one another?
- The paper states: “Protein expression values were first normalized across wells using an internal extension control (IgG antibodies conjugated with a matching oligo pair). Draw a cartoon of IgG antibodies conjugated with a matching oligo pair.
Learning Goals
Version History & Terms of Use
- Last updated May 2025
- Terms of use: This work is licensed under the Creative Commons Attribution-NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/.
Materials
- Instructor Guide
- Slide decks (with discussion prompts and embedded polling questions)
- Student worksheets
- Example Learning Management Services (LMS) pages for each lesson
- Note: See the Instructor guide for alternative approaches to teaching the module, including abridged versions
CREATE Method:
This 4-week module uses the CREATE method (Hoskins et al., 2007) to walk students through a scaffolded approach to reading Persistent serum protein signatures define an inflammatory subcategory of long COVID by Talla et al. (2023). Rather than read the paper in its entirety all at once, students walk through the paper section-by-section, completing worksheets and discussing with their peers along the way.
Throughout the module, students analyze the following methods:
- Olink proteomics for the detection of 1500+ proteins in participant serum samples
- Proximity Extension Analysis (PEA)
- Next Generation Sequencing (NGS)
- ELISAs
- K-means clustering
- Validation of results with an independent open dataset
