Phenomics x High-Throughput Imaging in 2D and 3D Culturing Models

Introduction

As a proud member of the Siriraj Center of Research Excellence for Systems Pharmacology (SiSP), a leader in high-throughput imaging, I am driven by a passion for uncovering the secrets of cancer biology. My work combines a hands-on approach in the lab with cutting-edge image analysis. I have specialized in exploring a wide array of cellular models, moving from the traditional 2-dimensional (2D) cell cultures to advanced 3-dimensional (3D) multicellular spheroids and multi-spheroid systems. This diverse experience allows me to tackle complex biological questions with creativity and precision. Beyond the visuals, I am also an expert in using RStudio to dive deep into the data, ensuring that every image tells a complete and compelling story.

Model 1: 2D Cancer Cell Culturing Model

1.1 Analysis overview:

In this model, my work centered on utilizing cancer stem cell (CSC) reporter systems to study the dynamics of cancer cells in a flat, 2D environment. I employed high-content real-time imaging to precisely monitor the CSC phenotype and understand its behavior over time.

A key part of this work involved a high-throughput drug response study, where I evaluated the effects of various drugs on cancer cells, with a specific focus on their impact on the CSC population. Using this approach, I was able to establish a direct link between CSC biology and drug response. By analyzing the data, I could determine how much a specific drug concentration killed the cells, and in parallel, how that same concentration affected the percentage of cancer stem cells.

I also developed a detailed step-by-step methodology to create an algorithm that could compare the effects of different drugs on the CSC proportion across a comparable dose range. This systematic approach allowed for a robust comparison of drug efficacies from the perspective of both general cell death and the specific targeting of the CSC population. The imaging data was then analyzed using Columbus™ Image Data Storage and Analysis System (PerkinElmer), enabling a sophisticated, single-cell level analysis. The resulting data was further explored and visualized using RStudio, revealing a comprehensive view of drug efficacy and its influence on CSCs.

1.2 Key Applications:

• Monitoring CSC Phenotype: Used a reporter system with high-content real-time imaging to precisely monitor the behavior of cancer stem cells (CSCs).

• High-Throughput Drug Screening: Performed drug response studies to evaluate the effects of various drugs on cancer cells, with a specific focus on their impact on the CSC population.

• Linking CSC Biology and Drug Response: Established a direct link between a drug’s concentration, its cytotoxic effect, and its parallel impact on the percentage of CSCs.

• Algorithmic Drug Comparison: Developed a detailed methodology to create an algorithm for comparing how different drugs affect the CSC proportion across a comparable dose range.

1.3 Methods and Tools:

• Wet Lab: Cultured CSC reporter-containing cell lines for phenotypic observation and drug response studies.

• Imaging: Performed high-content and high-throughput imaging, including real-time, live-cell, and fixed-cell techniques.

• Image Analysis: Conducted single-cell level analysis using Columbus™ software (PerkinElmer).

• Data Analysis: Utilized RStudio for further data exploration, visualization, and statistical analysis.

Model 2: 3D Cancer Cell Culturing Model (3D Multicellular Spheroid, 3D Multi-Spheroid)

2.1 Analysis overview:

To create a more realistic and complex environment for cancer studies, I developed and investigated advanced 3D culturing models, including both 3D multicellular spheroids and multi-spheroid systems. This approach allowed me to move beyond the limitations of 2D culture and study CSC phenotypes within a more natural, three-dimensional context using high-content imaging.

I also conducted tumorigenic assays, which are crucial for understanding the potential of cancer cells to form tumors. This was done with high-throughput imaging and subsequent image analysis at the single spheroid level using PerkinElmer’s Columbus software. This detailed analysis provided valuable insights into the growth and behavior of tumors in a 3D setting. The data was then rigorously analyzed using RStudio to draw meaningful conclusions about the tumorigenic potential of the cells.

2.2 Key Applications:

• Investigating CSC Phenotype in 3D: Used high-content and high-throughput imaging to study CSC phenotypes in a more realistic 3D context, including multicellular spheroids and multi-spheroid systems.

• Tumorigenic Assays: Performed high-throughput imaging-based tumorigenic assays to understand the potential of cancer cells to form tumors.

• Linking CSC Biology and Drug Response: Established a direct link between a drug’s concentration, its cytotoxic effect, and its parallel impact on the content of CSCs in the 3D contexts, including multicellular spheroids and multi-spheroid systems.

2.3 Methods and Tools:

• Wet Lab: Created and cultured 3D multicellular spheroids and multi-spheroid systems.

• Imaging: Performed high-content and high-throughput confocal imaging on live and fixed cells.

• Image Analysis: Conducted single-spheroid level analysis using Columbus™ software (PerkinElmer).

• Data Analysis: Utilized RStudio for rigorous analysis to draw meaningful conclusions about tumorigenic potential.

1.3 What I have learned from this data set

This transcriptomics analysis marks a significant milestone in my computational biology portfolio, representing a journey of deep exploration and reflection. I meticulously examined each step of the process, documenting my insights and rationale along the way. These detailed explanations can be found within the project files, providing a comprehensive guide to my analytical approach.

Furthermore, a question that arose during the analysis sparked the creation of a blog post: “What are GO, msigDB, KEGG, ORA, and GSEA in transcriptomics analysis?” This post delves into the intricacies of these essential tools and concepts, offering a valuable resource for anyone navigating the world of transcriptomics.

Acknowledgement

• I would like to thank my co-authors and colleagues. 🏀 Supawan Jamnongsong, a high-content imaging specialist and a wonderful senior who always provides comments, suggestions, and assistance.
  🏀 Pornlada Likasitwatanakul, a talented medical student who I knew could do both wet lab and image analysis from the moment we met.
  🏀 It was such a great and exciting time working with you all. I look forward to our next collaboration soon! Haha 😆😆 “Let’s play together!”

• I would like to thank my teachers and my co-advisors.
  🚀 Prof.(Emerita) Marianne Hokland, who encouraged me to perform 3D culturing experiments.
  🚀 Asst.Prof.Dr. Somponnat Sampattavanich, who always provides comments and suggestions on image analysis.
  🚀 Asst.Prof.Dr. Methichit Wattanapanitch, who provided intensive and exclusive discussions from a healthy stem cell perspective.

• Lastly, I would like to thank my Ph.D. advisor, Assoc.Prof.Dr. Siwanon Jirawatnotai. Without him, I would not have discovered the exciting world of cancer biology. 🌞🌞 From that point, I expanded my interest to the -omic fields (including Phenomics) for a deeper understanding. 🤩🤩

Last note from me

• I’m sorry for coding resource. I will upload the R code to my GitHub as soon as possible. The code is very unreadable (even to me, haha 😆😆!), because these image analysis scripts were some of my very first coding projects. I was practicing by doing real-world data analysis (Yes!, my Ph.D. thesis), and looking back at it now, it feels like I’m recalling a previous life. That’s because of my poor coding practices at the beginning (when I didn’t have any support from an LLM at that time).
• Another publication which directly relates to this “Phenomics x High-Throughput Imaging in 2D and 3D Culturing Models” is under review. Once it is published I will update this page with more information. Please bear with me!🤗

I’m very happy 🥰 that you are visiting my computational biology portfolio and would be even happier if you could provide suggestions or feedback 🤩.

You can contact me through various online platforms here 📬 or leave a comment below using GitHub account. 👇🏼