Sapio Sciences Blog

Archive : March 2023

How AI will Impact the Science Laboratory? Here are 22 Examples.

03/31/2023

Artificial intelligence (AI) has the potential to revolutionize the use of data in science laboratories. By leveraging machine learning algorithms, AI can help scientists analyze data faster, identify patterns and trends, and make more accurate predictions. Here are four specific topics, with examples in each case, of how AI can be used in science laboratories:

Drug Discovery

AI can be used to help identify new drug candidates by analyzing large datasets of chemical compounds and predicting their potential efficacy. By training machine learning algorithms on existing chemical data, AI can identify patterns and predict which compounds are most likely to be effective, reducing the time and cost of drug discovery.

AI can measure the binding affinity of a drug by considering the features or similarities of the drug and its target [1].
AI can analyze the Drug Information Bank to predict therapeutic potential, identify drug targets, evaluate pharmacokinetics, and screen compounds [2].
Virtual screening uses AI-based algorithms to find potential lead molecules among millions [2].
AI can automate chemical syntheses, making the process faster and more efficient [2].
AI can classify various cell types, aiding in the discovery of new targets for drug development [2].
AI can be used for target selection and validation, compound screening, and lead optimization [2].
AI can take in vivo-relevant properties of drugs into account for better drug discovery approaches in the future [3].

References:

[1] Artificial intelligence in drug discovery and development
[2] 4 Application Areas of Artificial Intelligence in Drug Discovery
[3] Artificial intelligence in drug discovery: what is realistic…

Predictive Analytics

AI can be used to analyze large datasets of experimental data and make predictions about future outcomes. For example, AI could be used to predict the outcome of a chemical reaction based on the input parameters, identifying the optimal conditions for a desired outcome. Here are some specific predictive analytics use case examples:

AI-powered lab management systems can predict equipment failures, allowing for proactive maintenance and reducing downtime.
Predictive analytics can be used to forecast inventory needs, ensuring that the lab has the necessary supplies on hand.
AI can analyze historical data to predict future trends and patterns, allowing for better decision-making in the lab.
Predictive analytics can be used to forecast the success of experiments, helping researchers prioritize their efforts.
AI can predict the optimal conditions for an experiment, reducing the need for trial and error.

Quality Control

AI can be used to monitor and analyze data from laboratory instruments in real-time, identifying potential issues before they become problems. For example, AI could be used to monitor temperature and humidity levels in a laboratory, alerting scientists to any deviations from optimal conditions. Here are some specific quality control use case examples:

AI can automatically identify and analyze defects in manufacturing processes, allowing for quick identification and correction of quality control issues.
AI can monitor instrument performance and flag any deviations from optimal conditions, ensuring that results are accurate and consistent.
AI can predict the likelihood of batch failures based on historical data, allowing for preemptive action to be taken.
AI can automate the process of identifying and removing outliers in data, ensuring that only high-quality data is used for analysis.
AI can analyze data from multiple sources to identify correlations and patterns, allowing for better decision-making and more effective quality control measures.

Data Integration

AI can be used to integrate data from multiple sources, including laboratory instruments, databases, and external sources. By analyzing this data in real-time, AI can provide scientists with a more comprehensive view of their experiments, allowing them to make more informed decisions. Here are some specific data integration use case examples:

AI can integrate data from multiple sources, such as laboratory instruments, databases, and external sources, to provide a more comprehensive view of experiments.
AI can identify patterns and trends in large datasets, allowing researchers to make more informed decisions.
AI can analyze historical data to predict future trends and patterns, enabling more accurate forecasting and planning.
AI can automate the process of data cleaning and standardization, ensuring that all data is consistent and usable.
AI can integrate data from different stages of the research process, from initial experimentation to quality control and final analysis, providing a complete picture of the data.

Conclusion

AI has the potential to revolutionize the use of data in science laboratories, allowing scientists to analyze data faster, identify patterns and trends, and make more accurate predictions. From drug discovery to quality control, AI can be used in a wide range of laboratory applications, providing scientists with new insights and enabling them to achieve their research goals more efficiently.

Laboratory Information Management System (LIMS) Glossary – 75 terms to know

03/30/2023

Laboratory Information Management Systems (LIMS) are a critical tool for managing laboratory data and processes in a variety of industries, from healthcare to pharmaceuticals to environmental testing. As the use of LIMS has grown, so has the need for a standardized vocabulary to describe the many concepts and terms involved. In this blog post, we provide a glossary of 75 key LIMS terms, from “Analyte” to “User roles,” to help professionals in the field navigate this complex and rapidly-evolving area. Whether you are a LIMS user, administrator, or vendor, this glossary will provide a valuable resource for understanding and communicating about LIMS-related topics.

Analyte: A chemical substance or component that is being measured or analyzed in a sample.
Audit management: The process of managing and conducting internal and external audits of laboratory data and processes.
Audit trail: A record of all actions taken in a LIMS, including changes to data or settings, to ensure traceability and accountability.
Barcode: A machine-readable code used for identifying samples and other laboratory items.
Batch processing: The process of analyzing multiple samples simultaneously, often using automation to increase throughput and efficiency.
Calibration: The process of adjusting an instrument to ensure its accuracy and reliability.
Chain of custody: The record of custody, control, transfer, analysis, and disposition of samples and their related data.
Chain of custody tracking: The process of tracking the movement and handling of samples and data to ensure data integrity and security.
Compliance management: The process of ensuring that laboratory processes and data comply with relevant regulations and standards.
Dashboard: A graphical representation of laboratory data and metrics used to monitor laboratory performance.
Data archiving: The process of storing laboratory data for long-term preservation and access.
Data backup and recovery: The process of backing up laboratory data to prevent loss and recovering lost data if necessary.
Data integration: The process of combining data from multiple sources into a single database or system.
Data management: The process of collecting, storing, and organizing laboratory data.
Data mining: The process of extracting valuable information and insights from laboratory data.
Data validation: The process of verifying the accuracy and completeness of laboratory data.
Data visualization: The process of displaying laboratory data in a visual format, such as graphs or charts, to facilitate interpretation and analysis.
Electronic data capture (EDC): The process of collecting and managing clinical trial data electronically.
Electronic data interchange (EDI): The electronic exchange of laboratory data between different systems or organizations.
Electronic health record (EHR): A digital record of a patient’s medical history and health information.
Electronic laboratory reporting (ELR): The process of electronically reporting laboratory data to public health agencies.
Electronic lab notebook (ELN): A digital tool used to record and store laboratory data and observations.
Electronic laboratory notebook (ELN) templates: Preconfigured forms used to record and store laboratory data and observations.
Electronic signature: A digital signature used to authenticate laboratory records and data.
Electronic signature and record act (ESRA): A U.S. federal law that defines the legality of electronic signatures and records.
Instrument: A device used for analysis, such as a spectrometer or chromatograph.
Instrument calibration history: A record of the calibration activities performed on an instrument, which is used to demonstrate instrument performance and accuracy.
Instrument interfacing: The process of connecting laboratory instruments to the LIMS for data transfer and analysis.
Instrument maintenance: The process of maintaining and repairing laboratory instruments to ensure their reliability and accuracy.
Laboratory accreditation: The process of assessing and accrediting laboratories based on their compliance with established standards.
Laboratory automation: The use of technology to automate laboratory processes, such as sample preparation and analysis.
Laboratory data management system (LDMS): A software system used for managing and analyzing laboratory data, which includes LIMS, ELNs, and other laboratory systems.
Laboratory-derived test result (LDTR): A laboratory test result that is electronically transmitted to an electronic health record or other health information system.
Laboratory-developed test (LDT): A laboratory test that is developed and validated by a laboratory for its own use, rather than being commercially available.
Laboratory informatics: The field that focuses on the development and use of software and technology to manage laboratory data and information.
Laboratory information exchange (LIE): The exchange of laboratory data between different systems or organizations.
Laboratory information management software: A software system used to manage laboratory data, often incorporating features such as instrument interfacing, data analysis, and reporting.
Laboratory information portal: A web-based platform used to access laboratory data, reports, and other information.
Laboratory information system (LIS): A system used to manage and store laboratory data and information.
Laboratory inventory management: The process of managing and tracking laboratory inventory, including reagents, supplies, and equipment.
Laboratory order entry: The process of entering laboratory orders into a LIMS or other laboratory system, typically using electronic or automated methods.
Laboratory performance metrics: Quantitative measures used to evaluate the effectiveness and efficiency of laboratory processes and operations.
Laboratory proficiency testing: The process of evaluating the accuracy and precision of laboratory methods and procedures by comparing results to established standards.
Laboratory safety: The process of ensuring the safety of laboratory personnel and the environment.
Laboratory security: The process of protecting laboratory data and systems from unauthorized access, modification, or destruction.
Laboratory specimen management: The process of managing the storage, retrieval, and disposal of laboratory specimens and samples.
Laboratory result interpretation: The process of analyzing and interpreting laboratory results, often involving comparison to established reference ranges and clinical guidelines.
Laboratory results review and approval process: The process of reviewing and approving laboratory results before they are released to stakeholders.
Laboratory specimen management: The process of managing the storage, retrieval, and disposal of laboratory specimens and samples.
Laboratory workflow management system: A software system used to manage and optimize laboratory workflows, often incorporating automation and decision support tools.
Laboratory worklist: A list of laboratory samples or tests to be performed, typically organized by priority and status.
Laboratory work order: A document or request used to initiate laboratory work, such as sample analysis or instrument calibration.
Laboratory-based surveillance: The use of laboratory data to monitor and track the incidence and prevalence of disease or other health-related conditions.
Laboratory inventory management: The process of managing and tracking laboratory inventory, including reagents, supplies, and equipment.
Master data management: The process of managing and maintaining the accuracy and consistency of laboratory data across different systems and applications.
Method validation: The process of verifying the accuracy and precision of a laboratory method or procedure.
Quality assurance (QA): The process of ensuring that laboratory data and processes meet established quality standards.
Quality control: Measures taken to ensure the accuracy and precision of laboratory results.
Report: A summary of laboratory results generated by a LIMS, often in a standardized format for ease of interpretation and comparison.
Results reporting: The process of generating and distributing laboratory reports to stakeholders.
Results review: The process of reviewing laboratory results for accuracy and completeness.
Results review and approval: The process of reviewing and approving laboratory results before they are released to stakeholders.
Report: A summary of laboratory results generated by a LIMS, often in a standardized format for ease of interpretation and comparison.
Results review and approval: The process of reviewing and approving laboratory results before they are released to stakeholders.
Root cause analysis (RCA): The process of identifying the underlying cause of laboratory errors or problems, and implementing corrective actions to prevent their recurrence.
Sample: A physical specimen or material that is collected and analyzed in a laboratory.
Sample registration: The process of registering a new sample in the LIMS.
Sample tracking: The process of tracking the movement of a sample from one location to another within the laboratory.
Sample tracking software: Software used to track the location, status, and handling of laboratory samples and specimens.
Specimen accessioning: The process of logging a sample into the LIMS.
Standard operating procedure (SOP): A documented procedure that outlines the steps required to perform a laboratory process or task.
Test menu: A list of laboratory tests and analyses that are available in the LIMS.
User acceptance testing (UAT): The process of testing a LIMS system to ensure that it meets the requirements and expectations of its users.
User interface: The graphical interface that allows users to interact with the LIMS system.
User roles: Different levels of access and permissions granted to users of a LIMS based on their job functions and responsibilities

Useful related links:

Laboratory Informatics Institute: https://www.limsforum.com/
The Analytical Scientist: https://theanalyticalscientist.com/
American Society for Quality – Laboratory Quality: https://asq.org/quality-resources/laboratory-quality
Society for Laboratory Automation and Screening: https://www.slas.org/
Clinical Laboratory Management Association: https://www.clma.org/
Laboratory Equipment Magazine: https://www.labmanager.com/
Scientific Computing World: https://www.scientific-computing.com/
European Laboratory Robotics Interest Group: https://elrig.org/
Laboratory News: https://www.labnews.co.uk/
American Chemical Society – Division of Analytical Chemistry: https://www.analyticaldivision.org/
American Society for Clinical Laboratory Science (ASCLS) – https://www.ascls.org/
Clinical and Laboratory Standards Institute (CLSI) – https://clsi.org/

Why are lab informatics systems so hard for scientists to use?

03/27/2023

Science is built on a foundation of unanswered questions. The scientific method perfectly defines this charism of research and discovery: science begins by asking essential questions and doesn’t stop until they are answered. For centuries, scientists have followed this approach; beginning with a question, forming a hypothesis, and following it with thorough research and testing until a well-informed conclusion can be made.

However, when selecting a LIMS or ELN Lab Notebook solution, the approach is often quite different. Established system incumbents are often chosen with little question, and many of the market’s most widely adopted solutions are disconnected from scientific realities, leading to data access and searchability issues, workflow configurability, platform stability, and more. Worse, many labs don’t realize these shortcomings until they’ve undergone a long and painful implementation process, including significant customization work. The result? Scientists that don’t use the lab systems in place, solutions prone to breaking with each passing update, and organizations that are delayed in advancing their novel scientific work.

By applying scientific questioning to the solution search, scientists can eliminate buyer’s remorse, maximize return on investment, and choose the right platform for their lab’s needs, today and tomorrow.

The complete Buyer’s Guide to Science-aware™ lab platforms is here.

So, why are LIMS and Lab Notebook solutions so hard for scientists to use? Let’s ask a few questions that uncover ‘why’.

Why can’t my system handle my lab’s most complex requirements?

Science is more advanced than ever—with more complex molecules, operational processes, reporting requirements, and data sets. When the inherent complexity of the science is combined with an over-complicated LIMS or Lab Notebook the result is untenable… and unusable. The key to simplifying this complexity is providing a solution that leads with exceptional usability, enables total workflow configurability, and delivers powerful scientific tools out of the box—tools like NGS, flow cytometry, 3D plating, and others. Integrating tools into one common, easy-to-use platform ensures that the right capabilities are within reach when scientists need them. In addition, reusability powerfully supports the simplification of complex scientific requirements by making various scientific objects reusable across the workflow to ensure everything scientists are doing is free of redundancy and drives real progress.

Why can’t I adapt my workflows easily as the needs of my lab change?

As science evolves, many labs find that their LIMS and Labo Notebook solutions stay the same. These lab systems are not made to adapt. When they do, it is only with the help of significant technical resources and time—both delaying scientific progress. By contrast, a no-code solution embraces change as a natural prerequisite of a scientific platform, empowering scientists to make powerful workflow changes without requiring technical intervention. When a platform is made for adaptability, changes can be made without compromising the stability of the solution amid future upgrades—a challenge that plagues many of the market’s leading lab informatics platforms.

Why don’t my LIMS and Lab Notebook work the same way?

While a few lab informatics providers position their offerings as a platform, these platforms often consist of loosely connected solutions designed on different code bases at different times and often, even by different organizations. Their integration is surface-level, and their user experiences are truly disparate. Users are therefore left to log in and out repeatedly over the course of the day, to learn multiple systems, and to piece together a holistic view of their experiments, processes, and data. Conversely, a true platform will begin with a common data architecture enabled by a singular login and user experience across solution areas.

Why do I have more data than ever but less understanding than I need?

The scientific ecosystem has a surplus of data, primarily driven by the high throughput techniques and technologies that enable today’s advanced laboratory processes. And yet, much of this data is not accessible, nor is it standardized for analysis. Most LIMS and Lab Notebooks lack robust tools for built-in scientific data reporting and visualization, instead of outsourcing these functions to another solution that may not have been designed for science. By contrast, a platform that puts lab data first will unify access to all of the data in one searchable knowledge-graph and empower scientists to visualize and understand data scientifically.

Why aren’t my scientists using my Lab Notebook or LIMS?

Ultimately, these usability challenges manifest in a lack of scientific adoption. Scientists don’t use their LIMS and Lab Notebook systems because they don’t make the scientific process easier, faster, or better. But poor scientist adoption is the symptom, not the problem. The problem is a pervasive lack of science-aware™ solutions that deliver the capabilities needed to support modern scientists in their novel work.

What does it mean to be science-aware™, and why is it the key to scientific usability?

A science-aware™ platform simplifies life in the lab. It makes data easily accessible, workflow configurable to specific needs, scientific analytics convenient, experiments and processes reusable, and unifies the end-to-end operational experience to make work simple, predictable, and efficient. Would you like to learn more about the science-aware™ standard for lab informatics? Access this helpful buyer’s guide.

Would you prefer to speak to a Sapio representative about your laboratory’s goals for lab informatics? Contact a science-aware™ expert here.

Download the Guide to Science-aware™ lab platforms.

What is flow cytometry propidium iodide cell cycle protocol?

03/06/2023

The propidium iodide (PI) cell cycle protocol is a common flow cytometry application used to analyze the cell cycle progression of a population of cells. This protocol involves staining cells with PI, a fluorescent DNA binding dye that intercalates into double-stranded DNA, and then measuring the fluorescence intensity of the cells using flow cytometry.

Here is a general protocol for the propidium iodide cell cycle analysis:

Harvest cells

Harvest cells using standard cell culture techniques and suspend them in a single-cell suspension.

Fix cells

Fix cells with 70% ethanol or a similar fixative solution to stabilize the cell membranes and prevent DNA degradation.

Stain cells with PI

Resuspend cells in a buffer containing PI and RNase. PI binds to the DNA in cells, and RNase helps to remove RNA from the cells to prevent interference with the analysis. Incubate cells for at least 30 minutes at 37°C in the dark.

Analyze cells using flow cytometry

Analyze cells using a flow cytometer with excitation at 488 nm and emission at 585 nm. The fluorescence intensity of the stained cells is proportional to the DNA content of the cells. The flow cytometer generates a histogram of fluorescence intensity that represents the distribution of cells in different phases of the cell cycle (G0/G1, S, and G2/M).

Data analysis

Analyze the flow cytometry data using specialized software to calculate the percentage of cells in each phase of the cell cycle. The software can also generate graphs and other visual representations of the data.

Overall, the propidium iodide cell cycle protocol is a widely used technique for analyzing the cell cycle progression of a population of cells using flow cytometry.

Learn about Sapio Sciences’ flow cytometry data analysis tool here.

Common flow cytometry questions:

What is flow cytometry compensation?

Flow cytometry compensation is a process that corrects for spectral overlap between different fluorescent probes used in a flow cytometry experiment. Spectral overlap occurs when the emission spectrum of one fluorophore overlaps with the excitation or emission spectrum of another fluorophore, leading to signal bleed-through and inaccurate measurement of fluorescence intensities. Compensation involves subtracting the signal from each fluorophore from the signal detected in other channels to correct for this overlap.

Compensation is necessary in flow cytometry experiments where multiple fluorescent probes are used to label different cell populations or biomolecules. These probes emit fluorescence at different wavelengths, which can overlap with other probes or with the autofluorescence of the cells being analyzed. If compensation is not applied, the fluorescence intensities of the different probes will be inaccurate, leading to an incorrect interpretation of the data.

The compensation process is typically performed using control samples that are singly stained with each fluorescent probe or with a combination of two probes. These control samples are used to calculate the spillover coefficient or compensation matrix, which quantifies the amount of fluorescence signal that is detected in each channel due to spectral overlap from the other channels. The compensation matrix can then be applied to the data from the experimental samples to correct for spectral overlap.

Overall, compensation is an essential step in flow cytometry experiments that involve multiple fluorescent probes, as it ensures accurate measurement of fluorescence intensities and improves the quality of the data.

Learn about Sapio Sciences’ flow cytometry data analysis tool here.

Common flow cytometry questions:

What are flow cytometry applications?

Flow cytometry has a wide range of applications in many fields, including biology, medicine, and biotechnology. Some common applications of flow cytometry include:

Cell sorting

Flow cytometry can be used to sort different types of cells based on their physical and chemical properties, such as size, shape, and surface markers. Cell sorting is useful for isolating specific cell populations for further analysis or experimentation.

Immunophenotyping

Flow cytometry is commonly used to analyze the expression of cell surface markers on different cell types, particularly in immunology research. Immunophenotyping can provide valuable information on cell function, differentiation, and activation state.

Cell cycle analysis

Flow cytometry can be used to analyze the cell cycle progression of a population of cells, as described in a previous question. This technique is useful for studying cell proliferation and cell cycle regulation.

Apoptosis analysis

Flow cytometry can be used to detect apoptotic cells in a population based on changes in their physical and chemical properties. Apoptosis analysis can provide insights into cell death mechanisms and is useful in drug discovery and cancer research.

Intracellular protein analysis

Flow cytometry can be used to analyze intracellular proteins in cells, such as transcription factors or signaling molecules. This technique involves permeabilizing cells to allow antibodies to access intracellular proteins and is useful for studying signaling pathways and gene regulation.

Microbial analysis

Flow cytometry can be used to analyze microbial populations, such as bacteria or fungi, based on their size, shape, and fluorescence properties. This technique is useful for studying microbial ecology, pathogenesis, and antimicrobial resistance.

Overall, flow cytometry is a powerful tool for analyzing complex biological systems and has a wide range of applications in many fields of research and industry.

Learn about Sapio Sciences’ flow cytometry data analysis tool here.

Common flow cytometry questions:

How does flow cytometry work?

Flow cytometry works by using a specialized instrument called a flow cytometer to analyze and quantify the properties of individual cells or particles in a sample. The process involves several key steps:

Sample preparation

The sample is first prepared by treating it with fluorescent dyes or antibodies that bind to specific markers on the surface or inside the cell. These markers can be used to identify different cell types or measure other properties of the cells, such as DNA content.

Flow cell

The prepared sample is then loaded into a flow cell, which is a narrow, tube-like channel that allows the cells to flow in a single file stream. This stream of cells is then passed through a laser beam that excites the fluorescent dyes or antibodies, causing them to emit light that is detected by the flow cytometer.

Laser excitation

As the cells pass through the laser beam, they scatter light in different directions depending on their size, shape, and optical properties. The laser beam also excites the fluorescent dyes or antibodies, causing them to emit light that is detected by the flow cytometer.

Optical detectors

The flow cytometer contains multiple optical detectors that measure the light scattered by the cells as well as the fluorescent emissions from the dyes or antibodies. These detectors are able to measure several physical and chemical properties of the cells, including size, shape, granularity, fluorescence intensity, and DNA content.

Data analysis

The data collected by the flow cytometer is then processed and analyzed by specialized software, which can provide detailed information about the properties of the cells or particles in the sample. This information can be used to identify and quantify different cell types, measure cell cycle progression, assess protein expression levels, and analyze cellular signaling pathways.

Overall, flow cytometry is a versatile and powerful technique that allows for the rapid analysis of large numbers of individual cells, providing valuable insights into the complexity of biological systems.

Learn about Sapio Sciences’ flow cytometry data analysis tool here.

Common flow cytometry questions:

What is flow cytometry used for?

Flow cytometry is a versatile technique used for a wide range of applications in biological and medical research, as well as in clinical diagnostics. Some of the most common uses of flow cytometry include:

Cell analysis: Flow cytometry is widely used to analyze the properties of individual cells, including their size, shape, granularity, and surface markers. This information can be used to identify and quantify different cell types, such as immune cells, stem cells, or cancer cells.
Cell sorting: Flow cytometry can be used to physically separate different types of cells based on their properties. This is achieved by using the flow cytometer to detect and sort cells into different containers based on their fluorescence and other characteristics.
DNA analysis: Flow cytometry can be used to measure DNA content in individual cells, allowing researchers to study the cell cycle and cell division.
Protein analysis: Flow cytometry can be used to measure the expression levels of specific proteins in individual cells, providing insights into cellular signaling pathways and protein-protein interactions.
Microbial analysis: Flow cytometry can be used to analyze microbial populations in environmental samples, such as soil or water, providing insights into microbial diversity and community structure.
Clinical diagnostics: Flow cytometry is used in clinical settings for the diagnosis and monitoring of various diseases, such as leukemia and HIV/AIDS, as well as for assessing immune function in patients undergoing transplantation or chemotherapy.

Overall, flow cytometry is a powerful and versatile technique that allows for the rapid analysis of large numbers of individual cells or particles, providing valuable insights into the complexity of biological systems.

Learn about Sapio Sciences’ flow cytometry data analysis tool here.