Friday, April 5, 2019

Win Cash, or Better Yet, Bragging Rights! Enter BioTek’s 2019 Imaging Competition

If you've been following us for a while, you probably remember that last year we kicked off our 1st annual Imaging Competition called Imaging Perspectives. Researchers from around the world submitted images captured with their Cytation or Lionheart for a chance to win cash prizes and have their image featured in BioTek's annual wall calendar. Well, the competition proved a success! We loved seeing all of the images and learning about the various applications customers are running with their instruments. So much so that we decided to do it again!

Here’s your chance to show the world what you are working on, and to share the art and beauty that’s often found in science. If you have a favorite image (or three!) that you’ve captured using a BioTek Cytation or Lionheart imager, visit our contest page and submit your entry today! You could win one of three cash prizes (1st place = $1,000, 2nd place = $500, 3rd place = $250) as well as the chance for your image to be featured in our 2020 wall calendar.

To get your creative juices flowing, here are the top three photos from last year:

Imaging Perspectives 2018 Winners

You can click here to see all of our 2018 winners.

Entries for our 2019 competition will be accepted now through the end of July. We can’t wait to see what this year’s submissions will bring!!

Enter now at

By: BioTek Instruments, Tara Vanderploeg, Marketing Specialist

Friday, February 22, 2019

BioTek Insights User Group Meeting at NIH

On February 7th, BioTek Instruments held its inaugural user’s group meeting, Insights 2019, at the NIH campus in Bethesda, MD. The meeting offered a venue for the exchange of ideas and a chance to develop new collaborations among our customers. Seven BioTek customers presented their research, with topics ranging from novel imaging methods for the screening of drugs inhibiting the sickling of red blood cells, to machine learning for the quantification of morphology changes in zebrafish. Apart from the customer presentations, attendees participated in workshops that demonstrated the following applications:
  • Normalization of Agilent Seahorse OCR and ECAR data using cell counting with Cytation™ 5 
  • Barrier integrity assays using Mimetas OrganoPlate Technology and Cytation 1 
  • Automated media exchange for spheroid proliferation assays using MultiFlo™ FX and the AMX module 
  • Automated scratch assays in 24- and 96-well microplates using AutoScratch™ and Lionheart™ FX
Customers can continue this networking and collaboration through discussion groups on BioTek’s Customer Resource Center (CRC). View discussions and login to post or comment. If you’re not already a CRC user, register with a BioTek instrument serial number or customer number for immediate access.

Many thanks to our presenters and attendees for their participation in this event!

By: BioTek Instruments

Thursday, February 14, 2019

SLAS 2019: Time is money…

The old adage “Time is money” initially came to mind when I walked around the exhibition floor at the 2019 Society for Laboratory Automation and Screening (SLAS) conference in Washington DC. Attendees of this conference have embraced laboratory automation to its full extent. As I perused the floor and talked to different vendors, I couldn’t help but think that all of this laboratory automation equipment was designed for one purpose only: to save time…and time equals money.

It is easy to make this assumption. I used to watch the cartoon The Jetsons growing up and couldn’t help but think how nice it would be if we had Rosie, the robot maid in our house. Imagine all the time we would save by having a robot do our chores! The reason behind laboratory automation is a bit more complex than just saving time.

As a long time field sales representative for BioTek, I have seen my share of researchers make the assumption that laboratory automation is designed solely to help them save time. I would walk into labs and quickly scan the benches to see what was going on. Sometimes I would notice stacks of spent ELISA plates occupying the benches. I would immediately approach the PI or lab manager and asked if they have ever considered a BioTek automated microplate washer or dispenser to help them with their plate washing or dispensing needs. Many of these customers would laugh at me. The conversation would go a bit like this:
Me: I see you are running a lot of ELISA’s in your lab and don’t have a plate washer. How do you wash your plates?

Customer: Oh…we wash them by hand. It is a tedious process that takes a lot of time, but that is the way we have always done this.

Me: Have you ever considered a BioTek automated microplate washer? I see you have a BioTek microplate reader in your lab.

Customer: [Customer chuckles] We have grad students for that and grad student time is cheap. We don’t need a washer.

Me: What if I told you that an automated plate washer will not just save you time but that it can help your lab create more “publishable” results?

Customer: Tell me more….
The main reason behind laboratory automation has less to do with time than it does with consistency and reproducibility. Reproducible results are publishable results. In this example, an automated microplate washer for this customer’s ELISA plates would provide more consistent dispensing and aspiration of wash buffer into the ELISA plate leading to tighter CV’s.

BioTek booth at SLAS 2019

This brings me back to SLAS 2019 and some of the newer technologies we presented there. Systems like the BioTek AutoScratch™ were a big hit at the show. Scratch wound assays can provide cancer researchers with a way of quantifying how different conditions affect cell migration - an important element in the study of cancer metastasis.1 The typical “non-automated” method of creating a scratch in your cell monolayer requires the use of pipette tips and then manually scratching your cells to create a “wound” in the monolayer. Results can be inconsistent when this is done manually - varying downward pressure and scratch inconsistency can result in highly variable results as demonstrated in our recent application note.

After AutoScratch makes the perfect scratch wounds, you can load your assay plates on our BioSpa Live Cell Imaging System or Lionheart™ FX Automated Microscope. BioTek’s Gen5™ software can then use a predefined scratch assay application protocol to image and automatically quantify cell migration because we know exactly where the scratch is on every single well. Our customers at SLAS saw the value in a completely automated workflow solution for this application.

Another hit at SLAS was the new AMX™ Automated Media Exchange module for our MultiFlo™ FX system. Standard plate washers (such as the BioTek 405™ TS or EL406™) are very well established products for washing adherent or lightly adherent cells on microplates. The popularity of 3D cell cultures has required researchers to find new ways of washing non-adherent cells (e.g., spheroids) in microplates. Standard plate washers don’t do well with spheroids as the aspiration pressure would suck the spheroid out of the well. A gentler approach is needed.

I have seen researchers setup wash routines for spheroid washing on complex and expensive pipetting robots. I would describe this as the “killing a fly with a shotgun” approach. You would never use a complex pipetting robot for more standard plate washing routines.

Others have decided to take a step backward and go completely manual with this method; they use hand-held multichannel pipettes to wash their spheroid cultures. I would describe this as the “Karate Kid” approach…or “catching a fly with a set of chopsticks.”

Many of these researchers have explained to me that they have gotten very good with the manual approach and are pretty fast…even faster than the MultiFlo FX with AMX. The problem is consistency and reproducibility. You may miss a well here and there, skip a column on your plate or accidentally aspirate your spheroids when this is done manually. This is where the AMX comes in. It gives you consistent and reproducible results in an automated platform.

We had a great time presenting our new products at SLAS 2019 to our customer base that typically embraces automation. The next time you think about adding automation in your lab, think less about the time savings and more about how automation provides more publishable results!


By: BioTek Instruments,  Bikram Chakraborty, Product Manager, Commercial

Tuesday, February 5, 2019

The Joy of Counting: A recipe for accurate cell-based assays

We have a shelf in our family kitchen that is filled with beautiful books that have more than a few stains and singed pages – badges of honor for any respectable cookbook. Sadly, we only have one chef in my house and I am most certainly not it. Fortunately for all of us though, my wife loves to cook and is a master of culinary improvisation. In this way, our well-loved cookbooks act more as guides than sets of strict instructions to follow.

Often my enthusiasm (if not my skill) is called upon to assist in the preparation of ingredients. I will frequently ask the chef, “How much of this do you want?” and “Should I add more?” My questions are met with casual replies like “a bit more” or “that looks good.” As much as I admire the freewheeling nature of this process, as a scientist, I pine for precise, defined quantities – and the tools to measure them!

In the applications lab at BioTek, we design cell-based assays to run on our life science instruments, including the Cytation™ and Lionheart™ automated imaging systems. These powerful tools enable robust and reproducible measurements for a broad range of applications. One important variable in cell-based assays is the number of cells contributing to a signal of interest. Many factors can lead to variations in cell number across conditions, including pipetting errors and treatment-induced effects. Lacking a reliable method to quantify and adjust for these variations can generate misleading results.

Counting cells directly using automated image acquisition and analysis is the most straightforward and accurate method for measuring the size of a cell population. Coupled with powerful Gen5™ software, the Cytation and Lionheart systems deliver intuitive and accurate cell counts using both label-free and fluorescent label-based techniques. Cell counts provide a sensitive metric for cell proliferation and viability assays, as well as a powerful method for normalizing results across conditions and replicates.

There are a number of similarities between cooking a great meal and conducting a successful assay. Both require thoughtful selection of ingredients, access to the right tools, and recognition of the diverse factors that influence how these components come together. In science, accurate measurements – and adjusting for the variables that can affect them – are crucial for interpreting results correctly and the ability to reproduce them. And while preparing a delicious meal may not require precise measurements, appreciating all the elements that contributed to its creation certainly adds to the experience of those who get to sit down and enjoy it.

Below are just a few examples of how cell counting can be used to add accuracy and reproducibility to your cell-based assays…we hope you enjoy.

Quantitative Evaluation of Cell Proliferation Using Label-Free Direct Cell Counting

Label-free methods of measuring cell growth kinetics are preferable over the use of stains that can influence proliferation rates. Although confluence level can be used for some applications, cell counting is the most direct quantitative measure of cell proliferation over a broad range of cell population densities.

High contrast brightfield cell counts over time relative to confluence. A comparison of NIH3T3 direct cell counts and percent confluence over time demonstrate the different characteristics of the two cell growth metrics. Label free cell counts indicate robust logarithmic cell growth up to full confluence (dashed line).
Profiles from 5 drug concentrations demonstrate a cell type-dependent differential dose response. NIH3T3, HeLa and HCT116 cell proliferation profiles enable quantitative analysis of drug response. Cell counts per mm2 were calculated every 2 hours for 5 days or until cells reached full confluence.

Related Application Note: Kinetic Proliferation Assay Using Label-Free Cell Counting 

Evaluation of relative levels of apoptosis and necrosis in a population using cell counting

The use of apoptosis and necrosis fluorescent probes were used in combination with automated kinetic imaging to quantitatively assess the effects of known inducers of cell death in multiple cell lines. Label-free high contrast brightfield imaging is used to assess the total number of cells in the population, and fluorescent probes to quantify early stage apoptosis at the level of plasma membrane inversion of phosphatidyl serine, and plasma membrane rupture associated with necrosis. This allows for accurate determination of percent apoptosis and necrosis in each cell population throughout the experiment.

Image analysis of apoptotic and necrotic cells. HT1080 cells were treated with camptothecin in order to determine the effect of the drug on the apoptotic and necrotic response of HT1080 cells. The first row (A-C) shows pre-processed high contrast brightfield images along with GFP and PI at 0 (A), 12 (B), and 24 (C) hours after treatment. The next row of images (D-F) shows the primary mask surrounding each individual cell as delineated by the pre-processed high contrast bright field image. Row G-I shows the expanded mask which captures more of the cell area and encompasses the GFP and PI signal. Row J-L shows the apoptotic cells highlighted in blue. Row M-O indicates necrotic cells highlighted in blue.

HT1080 cells treated with Camptothecin. Apoptosis and necrosis increase in a dose dependent manner as demonstrated by time course (A,B) and dose response (C,D).
Related Application Note: Live Cell Imaging of Apoptosis and Necrosis

Data Normalization for cellular metabolism analysis

The real-time measurement of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) using the Agilent Seahorse XF analyzer are widely accepted as the industry standard for obtaining in vitro cellular metabolic parameters. The metabolic rate difference caused by cell amount variation can be eliminated by cell count-based normalization. Here counts of cells stained with Hoechst 33342 are automatically determined and integrated into the data reduction pathway using a Cytation Cell Imaging Multi-Mode Reader.

Example of Seahorse XF data normalization using in situ nuclear staining and in situ cell counting. (A) Raw OCR and ECAR change with serial injections of oligomycin/FCCP (green arrow) and Hoechst 33342 (red arrow) before normalization. (B) Representative images of nuclei fluorescently labeled by Hoechst 33342 injection (upper panel) and nuclei identified and outlined by Gen5 software (lower panel). (C) OCR and ECAR normalized by in situ cell counts using Cytation Cell Imaging Multi-Mode Reader.
Related Application Notes: Normalization of Agilent Seahorse XF Data by In-situ Cell Counting Using a BioTek Cytation 5, XF Data Normalization by the Agilent Seahorse XF Imaging and Normalization System

By: BioTek Instruments, Joe Clayton, PhD., Principal Scientist

Friday, January 4, 2019

How Cell Counts Provide More Context to Your Data

A couple of months ago I, like most of the country, was captivated by the $1.6 billion dollar lottery drawings. After plunking down my $10, I let myself dream about hitting that jackpot and all the trappings of immense wealth. My mind drifted as I could smell the ocean standing on the lanai of my beachside home on some distant Hawaiian Island. Eventually snapped back to reality (Boston traffic does that to you quickly) – I realized I fell into a common pitfall – focusing on a result without accounting for variables. In the case of the lottery, the biggest variable being the odds of winning. With a possible 302 million combinations, the odds of hitting my numbers were about 6 times higher than getting killed by a falling coconut (1 out of 50 million).

All of this discussion of odds got me thinking about data sets I see at our customers’ sites. I often glance at heat maps of raw data as they are generated from our instruments. The high contrasting colors always look promising at first, but unlike the lottery, as scientist we are trained to account for variability. One critical variable in any cell-based assay is that differing numbers of cells between individual assay points can skew data. Typically cell cultures are optimized to account for proliferation rates, rate of cell death, cell adhesion so that cell numbers are consistent before performing any assays. Even with optimized conditions, there can be small variations that can affect the final cell density. These differences can ultimately cause increased variability across replicate data and throughout the experiment. Thus getting an accurate cell count within the assay well would allow for normalization of the data and remove this source of variability.

There are many strategies for normalizing cell population but the preferred method for normalization involves counting fluorescently stained cells in each well of a microplate via microscopy. With BioTek’s imaging and microscopy products, we provide an automated solution for cell counting that historically had been a tedious manual process or required the use of expensive imaging instruments. In the following example we use the nuclear stain DAPI to identify the cell nucleus then BioTek’s Gen5™ software automatically sets a threshold to provide accurate cell counts.

Figure 1: In-Situ Nuclear Staining (A) Threshold Analysis for object selection and count
As a Field Applications Scientist, I see firsthand how this powerful tool helps advance our customers’ research. Earlier this year, we launched a new integrated solution with Agilent Technologies that combined Agilent’s cellular metabolic analysis with BioTek’s imaging technologies. Centered around Agilent’s Seahorse XFe analyzers, customers can monitor real-time oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of live cells in a multi-well plate, interrogating key cellular functions such as mitochondrial respiration and glycolysis. When coupled with BioTek Cytation™ line, we now provide a powerful and intuitive turnkey platform for normalizing metabolic data. Perhaps the most impressive part of the system is how seamless the interface and workflow is for the customer. With a unified software look and feel provided by Agilent (with Gen5 powerful tools running in the background), the image based normalization is initiated with a simple barcode scan and a few confirmation checks. All of the data is centralized into the Agilent Wave software for complete analysis and interpretation. When the Agilent system is not active, the customer can utilize the Cytation as a stand-alone imager for other quantitative microscopy applications.

For more detailed information, see the XF Data Normalization by the Agilent Seahorse XF Imaging and Normalization System Application Note.

By: BioTek Instruments, Jared Amuan, Field Application Scientist

Tuesday, November 20, 2018

2018 SfN Meeting: Remembering Automated Microscopy in your Dementia Research

Dementia is a class of mental process disorders for which there are currently little to no treatment options. This is especially true in the later stages of Alzheimer's, a significant target for new treatments. At the 2018 Society for Neuroscience Conference, a commonly used model was presented containing three pathological components of Alzheimer's research: amyloid-β plaques, tau-containing neurofibrillary tangles, and microglia activation. In each one of these three research fields, the automated microscopy and plate reading functionality that BioTek Instruments provides will continue to play a crucial role in both qualitative and quantitative analyses. The combination of fixed and live cell samples will require both versatile and sensitive instrumentation to advance research aims.

These three Alzheimer's pathological pathways have their own unique biomarkers and analytical questions. In amyloid-β and tau neurofibrillary tangles research, a common assay presented was the measurement of the accumulation and structure of these proteins. For the larger samples of neurological tissue, both immunofluorescence and immunohistochemistry techniques continue to assess the progression of the disease such as gross production of plaques and fibrillary aberrations. At the intracellular level, microscopy is used to track molecular dynamics to understand the role of both localizations and posttranslational modifications in disease progression and treatments. The establishment of the role of microglia in Alzheimer’s disease is playing a critical role in progressing disease models and innovative treatments. Not only can fluorescent microscopy be used to examine defining cell type markers and molecular changes in fixed samples but live co-cultures are also used to examine intercellular interactions. Localizing and measuring the proximity of different cell types in addition to the simultaneous imaging of neuronal phenotypes are unraveling a complex and important story that may help many in the near future.

By: BioTek Instruments, Tom Lampert, Field Applications Scientist

Thursday, November 15, 2018

Neuroscience 2018 - Making Connections: From Cajal to the Connectome

When brain cells are wired in a manner that strays from their normal connectivity, functional consequences arise. Abnormal neuronal morphology and connectivity has implications in a wide variety of brain diseases and disorders from neurodevelopmental disorders to cognitive ageing. Collectively known as the connectome, researchers have been mapping neuronal connections in the brain for almost 150 years. Since the days of Neuroanatomist Santiago Ramón y Cajal, neuroscientists have been drawing and mapping the structure of neurons and other brain cells in an attempt to elucidate their function and understand how the brain works. Little did Cajal know back in 1906, that his methods of archiving neurons would change very little into the 21st century. Despite advances in techniques to label neurons, and although new computational methods to draw their morphology have replaced the Golgi stain and the paper and ink Cajal used, not much else has changed. His work is still very much relevant today and neuroscientists still draw each neuron manually, albeit digitally. Interestingly in this age of bioinformatics, the field is still struggling to find a way to archive and connect the input/output trees of all of the neurons researchers have drawn by hand in software packages such as Neurolucida or ImageJ/Fiji.

Santiago Ramón y Cajal, Golgi stained pyramidal cells of the cerebral cortex (detail) ink and pencil on paper.
 Courtesy of the Cajal Institute, Spanish National Research Council, or CSIC. Madrid. Spain.
The process of labeling neuronal nuclei is straightforward, inexpensive and accomplished in a variety of ways. Neuronal nuclei in a brain tissue section or in a 2D neuronal culture are visualized using fluorescence markers or colorimetric detection such as DAB and Nissl staining. With the exception of slight size differences, all neuronal nuclei are relatively similar in morphology.

Fig 1: Left- H&E stained sagittal mouse brain tissue section, montaged, stitched
4x on the Cytation™ 5, right-inset, digitally zoomed hippocampal region
Only when you begin labeling the processes of these cells, do their differences in structure begin to unfold. Furthermore, because of the sheer number of connections in what appears to be a tangled web of dendrite and axonal networks, untangling these connections to make sense of this wiring diagram introduces an enormous amount of difficulty and complexity. From images like these (Fig. 2) however, researchers can screen their targets in a high throughput manner using different HTS imaging platforms, without the need for laborious, manual tracing. Assays like these provide readouts for metrics like global neurite outgrowth, synaptogenesis, differentiation and phenotypic analysis such as the one presented at Neuroscience 2018 by C. Pandanaboina et al.

Fig 2: primary culture of neurons and astrocytes at 20x on the Cytation™ 5 labelled with
MAP2 (red) and GFAP (green) respectively and nuclei counterstained with Hoechst.
However, there will always be a need to dissect brain circuitry to parse out different aspects of development and disease. Likewise, in order to accomplish this, open access to findings will be key in understanding complex brain networks. Researchers in a wide variety of neuroscience disciplines are finally coming together in a concerted effort to build a central resource to assess these complexities using computer models and machine learning algorithms. Ten years ago, Giorgio Ascoli and researchers at Krasnow Institute at Georgetown University launched a digital repository for anyone who created neuron tracings in any species or region of the brain to create a database of sharable, downloadable content. It is one of the only digital neuronal reconstruction databases to date. Giorgio presented some of the challenges and accomplishments of their work recently at the Neuroscience 2018 meeting in San Diego. These methods are still quite labor intensive and expensive. From the nearly 100,000 neuron trees in the database, it has taken researchers over 100,000 years cumulatively and $2.5 million dollars in research funding.
Fig 3: Human, male, neocortical, pyramidal neuron from the database.
NeuroMorpho is currently collaborating with BigNeuron project, from the Allen Institute for Brain Science, and is another potential resource. Along with several other online 3D reconstruction resources such as MatLab (MathWorks), Vaa3D and Imaris (Bitplane), users can upload their tracings to the NeuroMorpho.Org database. This will help the community build a curated neuronal network across species and brain regions in hopes of connecting morphologies to subtle functional consequences.

  1. Ascoli, G. A., Maraver, P., Nanda, S., Polavaram, S., & Armañanzas, R. (2017). Win–win data sharing in neuroscience. Nature Methods, 14, 112. Retrieved from 
  2. Akram, M. A., Nanda, S., Maraver, P., Armananzas, R., & Ascoli, G. A. (2018). An open repository for single-cell reconstructions of the brain forest. Scientific Data, 5, 180006.
Further inquiry and reading resources: 
  1. Meijering, E. (2010), Neuron tracing in perspective. Cytometry, 77A: 693-704. doi:10.1002/cyto.a.20895 

By: BioTek Instruments, Sarah Guadiana, Ph.D, Field Applications Scientist, Imaging