Biomarker Quantitation In The Cloud

The Transcriptic robotic cloud lab provides a great advantage for large-scale biomarker assays, delivering consistent sample to sample processing with robotic reliability, all on demand when you need it. We’ve enabled both large pharma and small biotech to quantify DNA, RNA and proteins from hundreds or thousands of samples in an automated and programmatically driven way.

In this post I want to focus on our commitment to providing access to modern tools in biomarker quantitation, and how integrating these tools with the Transcriptic robotic cloud lab not only increases accessibility to modern tools but also leverages the repeatability and reliability of an entire experimental workflow from treatment to analysis.

In the background, the robotics, software and science teams, at Transcriptic, have been collaborating on bringing the Mesoscale Discovery Sector S 600 online to all Transcriptic users. Today I’m happy to announce that we’ve made huge strides in enabling protein quantitation in high-throughput for our users and this is a big step in expanding that capability.

Mesoscale Discovery (MSD) Sector S 600 integrated with the Transcriptic robotic cloud lab

Mesoscale Discovery (MSD) Sector S 600 integrated with the Transcriptic robotic cloud lab

The Sector S 600 performs a function similar to traditional ELISA though instead leverages electrochemiluminescence (ECL) as a detection technique. There are a number of particular benefits to the Sector S 600 but one of the most compelling is the data to sample efficiency. This makes the Sector S 600 invaluable for those liquid or tissue biopsies that are low in quantity, such as plasma.

To achieve high levels of analyte quantitation per unit sample, the Sector S 600 uses a proprietary plate technology that consists of microplate wells modified with 10 capture antibody spots in each well. This spot array of capture antibodies enables up to 10-plex capture of target analytes all from the same sample volume in a single well. Once the target analytes have been captured on the spots the plate is washed and detection antibodies are added similar to an ELISA workflow. At this point, the device will sequentially read each spot and measure the resulting electrochemiluminescence (ECL).

In addition to the high data to sample efficiency, the MSD Sector S 600 also touts a 6-log dynamic range enabled by the ECL labelling technology, rendering even extremely low concentration analytes in your samples, detectable. Pre-validated panels in MSD’s V-plex line provide panels of 10 or more targets in human, mouse, or rat samples in the following areas:

  • Proinflammatory response
  • Neuroinflammation and Alzheimer’s
  • Cytokines
  • Chemokines

MSD also provides customizable plates in their U-plex line, where we can onboard your custom panel of targets for your biomarker quantitation workflow. Launching runs that use the Sector S600  from the Transcriptic web application is just like any other run, and the team produced a great interactive UI for quickly assessing the multiplexed panel data.

Transcriptic is happy to make the Mesoscale Discovery Sector S 600 immunoassay device available in the cloud.  It is fully integrated with our robotic cloud lab platform delivering seamless sample to data from tissue biopsies through to multiplexed protein quantitation outputs. The Sector S 600 sports a 6-log dynamic range perfect for low concentration analytes and up to 10plex analyte detection per sample for efficient data to sample material yields.

Protein quantitation with Mesoscale Discovery Sector S 600

  • Low sample quantity demands with up to 10-plex detection of targets
  • High-throughput in either 96 or 384 well plates
  • High sensitivity with a 6-log dynamic range
  • Multiple panels available including inflammation and Alzheimer’s relevant targets across human, mouse, and rat

Get started measuring multiplexed protein biomarkers today, by getting in touch with us or learn more in the technical notes http://go.transcriptic.com/msd

New Subscription Tiers

SHARED INFRASTRUCTURE

The cloud lab model of Transcriptic ensures that the lab automation is used to its highest efficiency, this is enabled by having the robotic infrastructure as a shared resource consumed by all users. A result of this is that once runs are submitted they enter into a queue until enough robotic capacity is available for execution.

CHOOSING YOUR PRIORITY

With the introduction of tiers, organizations can now set the priority with which they wish their runs to have on the Transcriptic platform. Specific priorities broadly correlate to an average queue time so if you need your results extremely quickly you should opt for a higher priority tier. If your discovery pipeline can accommodate some delay you can opt for a lower priority tier.  At times when you have runs that need a faster turn around than your standard runs, you can opt to jump a tier for that month and go back to your standard tier the next month.

MULTIPLE TIERS TO CHOOSE FROM:

Tier Estimated Queue Time Monthly Annual plan, prepaid
1 1 week+ $600.00 $600.00/month
2 3-5 days $1,125.00 $1,068.75/month
3 1-2 days $2,250.00 $2,025.00/month
4 24 hours $4,500.00 $4,050.00/month
5† 8 hours $8,500.00 Get in touch
Private Workcell‡ <8 hours $25,000.00 Get in touch

† Yearly expected volume >$1M, ‡ Yearly expected volume >$3M, *Initial pricing subject to change.

READY TO GET STARTED WITH TRANSCRIPTIC?

Get in touch with our sales team at [email protected] to get started with the tier right for your organization.

FAQ

Q: How do I upgrade my tier?
A: Get in touch with your organization's admin who can upgrade your tier straight from the web application.

Q: What priority does my run have if I submit it then change my tier?
A: Runs receive the priority that the organization had during run submission.

Q: Is there a free tier?
A: Yes there is a free tier however, it does not come with the ability to submit runs.

Q: What is the benefit to paying annually?
A: Paying annually secures a discount on the tier fee along with simplified billing.

Q: Why do the private workcell and tier 5 have minimum spend requirements?
A: These two options are for users who anticipate intense usage of the robotic hardware. To ensure the platform is accessible to meet your requirements Transcriptic also requires commitment from the user.

Q: What happens if my run waits longer than the expected queue time?
A: Due the dynamic nature of the queue Transcriptic cannot guarantee queue times and can only quote historical performance of the tier to provide estimates.

Q: Can I only pay for the months I am running experiments?
A: If you are on a month to month subscription, please contact [email protected] to downgrade to an inactive tier. For monthly subscription billing with an annual commitment, it is possible to downgrade before the next year starts in your commitment cycle, however monthly billing will continue for the initial year commited to. 

QuikChange Lightning on Transcriptic

Part of growing Transcriptic means making industry leading protocols and reagents accessible to our users. For this reason I’m happy to announce that on November 4 2016 Agilent Technologies’ QuikChange Lightning, site-directed mutagenesis kit will be available in the Transcriptic protocol browser.

Earlier in the year, for the first time ever Transcriptic was used in a peer-reviewed study for the generation of a large number of mutants. This enabled the team from UC Davis to explore a parametric space they had previously not had access to. With this in mind we thought enabling the exploration of protein sequence-space was a great application of a programmable lab, as it marries protein engineering computational techniques with wet-lab experimentation.

We approached Agilent who were highly responsive in pursuing the implementation of their QuikChange products on Transcriptic. The first of which that we decided to tackle was QuikChange Lightning for single site-directed mutagenesis.

QuikChange is a very popular suite of kits that provide a highly efficient non-PCR method for reliable site-directed mutagenesis. The QuikChange kits make use of a linear amplification strategy with only the parental strand serving as the DNA template. The kits also feature highly efficient proteins for mutant generation and reaction clean up, which all lead up to a robust and simple user experience.

Schematic cartoon of the QuikChange mutagenesis process.

Schematic cartoon of the QuikChange mutagenesis process.

The implementation on Transcriptic is designed to make it exceptionally easy to generate anywhere from single mutants up to large numbers with minimal hands-on time from the user. A user simply launches the protocol, supplies a .csv list of mutagenic oligonucleotides and the source DNA (typically a plasmid) to mutate.

Once the run has been submitted, the Transcriptic platform takes care of ordering the oligonucleotides, performing the QuikChange reaction, transforming competent bacteria, picking colonies and finally growing the picked colonies in deep well liquid cultures.

A section of the resulting plate from transforming competent bacteria with the QuikChange reaction products. Colonies circled in red were picked by the platform to be grown in liquid culture.

A section of the resulting plate from transforming competent bacteria with the QuikChange reaction products. Colonies circled in red were picked by the platform to be grown in liquid culture.

When implementing partner reagents we have to ensure we can achieve the same data quality as our users are typically used to back at the bench. For QuikChange we replicated the performance of the kit by attempting a two adjacent base mutation from CA to GG. The protocol produced a large number of colonies and upon screening with sanger sequencing 75% of screened colonies were positive for the mutation.

a) Top: Sequence from the source DNA. Below: Sequences from 4 picked colonies. 3 out of the 4 screened colonies were found to be positive for the target mutation. b) Clean sequences traces shown for 3 colonies.

a) Top: Sequence from the source DNA. Below: Sequences from 4 picked colonies. 3 out of the 4 screened colonies were found to be positive for the target mutation. b) Clean sequences traces shown for 3 colonies.

Over the past 4 weeks, we’ve had the pleasure to talk about QuikChange lightning on Transcriptic at SynBioBeta San Francisco 2016, LRIG Boston 2016 and at iGEM 2016. All fantastic venues for sharing our work and with some very excited people in attendance.

We hope you’ll see how easy it is to start exploring a vast protein sequence-space with Transcriptic and QuikChange Lightning, you can learn more here learn.transcriptic.com

 

 

 

Introducing BSL-2 Workcell Instances

Since the start of Transcriptic users have only been able to conduct Biosafety Level 1 (BSL-1) experiments on our automated cloud infrastructure. We believed that executing well at this lower stringency threshold meant that we could deliver a great service for our users whilst still ensuring we could rapidly develop our capabilities.

A few months ago we completed the construction of 2 additional workcells, working closely with a select group of users we have been running automated BSL-2 experiments on our cloud infrastructure to facilitate some amazing science. With the completion of these new workcells and some great users we are really excited to be able to offer BSL-2 workcell instances to all Transcriptic users from today.

With access to automated, cloud BSL-2 environments companies using Transcriptic are tackling some of the hardest problems in discovery biology in completely new ways. As an example biological tissue can now be processed and analyzed at Transcriptic via techniques such as ELISA, qPCR, and RNA-seq in a executed in a completely programmatic and automated way where data are made available to our users through the API.  BSL-2 instances have also enabled new applications including viral engineering, mammalian cell based assays and BSL-2 bacterial engineering possible entirely from a command line interface. 

Get started with BSL-2 instances today

To start using BSL-2 environments for your work simply create a new project and upgrade it to BSL-2 status. Now, any run submitted to this project will be executed on a Transcriptic BSL-2 instance.

For more information on BSL-X environments at Transcriptic check out the documentation

Autoprotocol Summit 2016 - Pushing reproducibility and usability

We recently hosted the second annual Autoprotocol Summit at our new San Francisco office. With the gorgeous city of San Francisco as a backdrop, we took the day to think about how Autoprotocol has changed since the first Autoprotocol Summit in 2016, and how we can make it even better.

Joined by developers and vendors from a number of companies and organizations, we met to celebrate the achievements of the last six months and focus on making Autoprotocol more usable and reproducible. Here's a report from the trenches!

We began with a recap of the progress made since the last summit.

Highlights included:

  1. An increase in Autoprotocol's scientific coverage with 15 new Autoprotocol Standard Changes (ASCs) allowing for new instructions like magnetic transfer for DNA, RNA, protein, and cellular bead based purification to purification by gel electrophoresis.

  2. Improvements in developer tools for writing and analyzing Autoprotocol with the release of Autoprotocol-Utilities, updates to Autoprotocol-Python, and to the data analysis components of the Transcriptic Python Library (TxPy).

  3. The ability to increase reproducibility with the addition of time constraints as a top level feature in Autoprotocol.

Overall, it's never been easier to write and use Autoprotocol.

Next we took a look at how Autoprotocol is being used in the wild. We heard about all of the projects using Autoprotocol inside and outside of Transcriptic including autoprotocol-ruby, assaytools, and “How to make any protein you want for $360”.

Developer's perspective from Brian Naughton

Developer's perspective from Brian Naughton

Autoprotocol developer Brian Naughton presented his experiences using and contributing to Autoprotocol. Brian felt that Autoprotocol's underlying JSON and Python infrastructure was a strong choice for standard adoption because it was very accessible to scientists. Brian, who uses Autoprotocol mainly in conjunction with Transcriptic, also described how the development of TxPy has made it much easier to launch his experiments. Finally Brian concluded with a brief mention of his plans to look at generating sequences of experiments with workflow tools to make chained experiments a reality.

In the Q&A period, Brian touched on the need for greater transparency in how Autoprotocol is translated to a physical experiment (especially with regards to inventory) as well as the need for more tools to help abstract away some lower-level decisions which scientists may be less interested in (like liquid-handling parameters).

The Need for Platform Independence by Conner Warnock

The Need for Platform Independence by Conner Warnock

Next, Connor Warnock from Notable Labs brought a vendor's perspective to the day. Connor shared a common pain point faced by many automation startups: the lack of standardization around devices and their protocols. His presentation focused on the possibility for Autoprotocol to become a universal common interface and compared the current stage of Autoprotocol to the early days of HTTP, where the long-term payoff is clear, but more immediate payoff is required for driving adoption. As a part of becoming a better layer for lab automation, there was substantial discussion around the possibility of broadening the scope of Autoprotocol to cover open-sourcing Lab Inventory Management Systems (LIMS) as well as open-sourcing device-driver wrappers.

After the two presentations, everyone headed to the Goals session excited to move Autoprotocol forward. After a short icebreaker, we went into brainstorming mode to discuss the problems which are confronted by Autoprotocol's users and potential users. There was a lively discussion with potential users ranging widely from pharmaceuticals to developing countries to even the FBI. The brainstorming led to three key areas for future Autoprotocol growth:

  1. Visualization and Intent

  2. Reproducibility

  3. Community Adoption

With the topics to be tackled agreed upon, we broke for lunch and started informal discussions.

Following lunch, we looked into the possibility of improving the ASC contribution process. There was general consensus that the current process should be made more transparent and explicit. As one action item moving forwards, the public discussion around submitting new ASCs will begin much earlier in the developers forums, and interested parties should join in on the contribution process. There was also substantial discussion on changing the underlying format of Autoprotocol to more naturally support the communication of experimental intent which would make it easier and more natural for scientists to use Autoprotocol for communicating scientific protocols.

Finally, we broke into small groups to tackle the three key areas of Visualization, Reproducibility, and Community Adoption brought up earlier. The room came up with a lot of great ideas and rapid prototypes. To highlight a few, there were mocks of better protocol visualization tools, a drag-and-drop system for creating Autoprotocol, and the groundwork for more comprehensive standards for reagents and data. Be on the lookout for more specific implementations to come!

Thank you all who attended Autoprotocol Summit 2016. None of the growth in Autoprotocol would be possible without the enthusiasm and care of the developer community. Shoutouts go to external developers such as Brian Naughton and Transon Nguyen & Connor Warnick from Notable Labs. Thanks as well to Ben Miles and Yang Choo for leading and organizing the sessions and Taylor Murphy for handling the logistics. Thanks also for the support of the Autoprotocol Curators including Tali Herzka from Verily and Vanessa Biggers, Jeremy Apthorp & Peter Lee from Transcriptic. And of course, a big thank you to the Autoprotocol community. We are looking forward to another great year of Autoprotocol ahead.

 

Transcriptic Launch

Easier development of packages

The Transcriptic CLI tool was recently updated to make protocol package development faster. The new launch command quickly allows you to preview the UI generated by the package as well as the autoprotocol JSON.

Packages

Transcriptic packages enable the launching of protocols via the UI. These are really useful for sharing protocols within your team if members of your team don't program their own protocols. They can easily launch runs with custom parameters all from a user interface.

The process for getting packages on to Transcriptic

Currently packages are uploaded to Transcriptic via a release process to enable version control, however this process can be some what slow when developing a package. Often you would want to quickly make changes and see how they effect the user interface generated, or have GUI access to your inventory when launching a protocol without going through the release process. The new launch command should make this easier.

Transcriptic launch

Executing the new launch command will open up a web browser and show the UI generated from the manifest.json file in the package. Here you can interact with the user interface like you would for any other package, by filling in parameters and accessing containers in your inventory via the inventory browser.

During package development you can launch your protocol as follows:

transcriptic launch protocolName --project "Project Name"

This then opens up your default web browser and will show you the run configuration page that is constructed from the manifest.json.

Opening https://secure.transcriptic.com/swiftpharma/p1584739745vfz/runs/quick_launch/ql384029n9vp28m
Waiting for inputs to be configured.......
Generating Autoprotocol....

Interact with the generated UI as you would with any other package.

Now head back to the command line to interact with the Autoprotocol JSON

Now head back to the command line to interact with the Autoprotocol JSON

From here you can fill out the parameters for the experiment as well as populating fields with samples from your inventory. When you 'Generate protocol input', the run is not submitted to Transcriptic. Instead, the JSON generated by the protocol is then available at stdout back in the command line. Where it can be piped to transcriptic submit or to a log file locally.

{
  "refs": {
    "tube": {
      "new": "micro-1.5",
      "store": {
        "where": "cold_4"
      }
    }
  },
  "instructions": [
    {
      "to": [
        {
          "volume": "2.0:microliter",
          "well": "tube/0"
        }
      ],
      "op": "provision",
      "resource_id": "rs18nw6ta6d5bn"
    }
  ]
}

This means it is very easy to inspect the autoprotocol generated with a package or quickly go through iterations of the fields available in the user interface speeding up development time.

Programming Transcriptic

So you want to program a biology lab? You're in the right place.

Today we are going to instruct a completely automated robotic cloud lab to grab a genetically modified strain of bacteria from a library of common reagents, innoculate some bacterial growth media and finally watch how that culture grows over 8 hours by seeing how the bacteria scatter 600nm light.

Let's get started.

After you sign up for a Transcriptic account you need to install some dependencies, we'll be working with Python today so pip is your friend.

First let's install the Transcriptic CLI tool.

pip install transcriptic

Next we'll be writing Autoprotocol the open standard for experimental specification so we need a tool to help us do that.

pip install autoprotocol

OK we're all set.

Let's run the Transcriptic CLI.

> transcriptic

Usage: transcriptic [OPTIONS] COMMAND [ARGS]...

  A command line tool for working with Transcriptic.

Options:
  --apiroot TEXT
  --config TEXT            Specify a configuration file.
  -o, --organization TEXT
  --help                   Show this message and exit.

Commands:
  analyze         Analyze a block of Autoprotocol JSON.
  build-release   Compress the contents of the current...
  compile         Compile a protocol by passing it a config...
  create-package  Create a new empty protocol package
  create-project  Create a new empty project.
  delete-package  Delete an existing protocol package
  delete-project  Delete an existing project.
  format          Check Autoprotocol format of manifest.json.
  init            Initialize a directory with a manifest.json...
  login           Authenticate to your Transcriptic account.
  packages        List packages in your organization.
  preview         Preview the Autoprotocol output of protocol...
  projects        List the projects in your organization
  protocols       List protocols within your manifest.
  resources       Search catalog of provisionable resources
  submit          Submit your run to the project specified.
  summarize       Summarize Autoprotocol as a list of plain...
  upload-release  Upload a release archive to a package.

First we need to login to our Transcriptic account and specify our organization.

> transcriptic login

Email: [email protected]
Password:
You belong to 3 organizations:
  Sanger Lab (sanger-lab)
  Franklin Lab (franklin_lab)
  Swift on Pharma (swiftpharma)
Which would you like to login as [sanger-lab]? swiftpharma
Logged in as [email protected] (swiftpharma)

Great we're logged in, now we can start writing our protocols. Let's create a file to contain our commands to produce the Autoprotocol description of a growth curve. You could also do this interactively in a Python REPL.

> touch growth_protocol.py

First we'll add the import statements, autoprotocol is needed to provide the functions to generate Autoprotocol JSON. And the JSON package is required for some utility methods to handle parsing JSON.

"""
growth_protocol.py

This script produces autoprotocol to execute a growth curve on Transcriptic
"""
from autoprotocol import *
import json

Next let's instantiate a protocol object that all of our instructions are attached to.

p = Protocol()

Now we are going to begin defining the references. References describe containers used in the protocol such as plates and tubes. We are just going to describe 2 containers 1 for the bacteria and one for the plate that will be used in the plate reader to follow along the growth.

A reference takes 5 arguments, name, id, cont_type, storage and discard. id is required if referencing a container that already exists, if instantiating a new container an id will automatically be assigned by Transcriptic upon run submission. cont_type is the type of container, below we are specifying a flat bottomed 96 well plate and a 1.5 mL microcentrifuge tube. storage is the temperature at which you require the sample to be stored when not directly in use. Below the plat will be stored at 4C whenever it is not being used, the tube however will be discarded at the end of the run as discard is set to True.

growth_plate = p.ref("growth_plate", id=None, cont_type="96-flat", storage="cold_4", discard=None)

bacteria = p.ref("bacteria_tube", id=None, cont_type="micro-1.5", storage=None, discard=True)

Now that we have defined our containers we now want to fill them up. First of all we want to get some E. coli from the Transcriptic common reagent library. This can be done with the provision instruction and the resource_id for the material we need. Resource IDs can be found in the catalogue.

dh5a = "rs16pbj944fnny"
p.provision(dh5a, bacteria.well(0), "15:microliter")

Now let's fill the first column of that empty 96 well plate with some growth media. LB-broth should do the job nicely. The code below will dispense 175µL of LB-broth into each well in the first column.

p.dispense(growth_plate, "lb-broth-noAB", [{"column": 0, "volume": "175:microliter"}])

Now let's innoculate 4 of the 8 wells with E .coli using transfer.

test_wells = growth_plate.wells_from(0, 4, columnwise = True)
for dest in test_wells:
  p.transfer(bacteria.well(0), dest, "2:microliter")

In a interactive python session you can see what the test_wells``WellGroup looks like. Note that wells are 0 indexed and increment row wise.

>>> test_wells
WellGroup([
  Well(Container(growth_plate), 0, None),
  Well(Container(growth_plate), 12, None),
  Well(Container(growth_plate), 24, None),
  Well(Container(growth_plate), 36, None)
  ])

That's the innoculation taken care of now let's create a loop that will incubate the culture for 30 minutes then take an absorbance measurement at 600nm.

# Set total growth time of growth curve
total_growth_time = Unit(8, "hour")
# Set the number of OD600 measurements taken over the time course.
number_of_measurements = 16

for i in xrange(0, number_of_measurements):
  p.cover(growth_plate) # put a lid on the plate
  p.incubate(growth_plate, "warm_37", duration=total_growth_time/number_of_measurements, shaking=True, co2=0)
  p.uncover(growth_plate) # take lid off of plate
  p.absorbance(growth_plate, measurement_wells, wavelength="600:nanometer", dataref="od600_%s" % i)

Now with all of these in a single python file we need to get some JSON that can be sent to the Transcriptic API.

For this we can use:

# Dump the Autoprotocol JSON.
my_experiment = json.dumps(p.as_dict(), indent=2)
print(my_experiment)

Now from the command line we can run the python file which will print the JSON object to stdout. The stdout can be piped to the Transcriptic CLI.

Let's see how much this protocol run will cost with transcriptic analyze

python growth_protocol.py | transcriptic analyze
✓ Protocol analyzed
  67 instructions
  2 containers
  Total Cost: $25.59
  Workcell Time: $18.25
  Reagents & Consumables: $7.34

Let's find the project we want to submit to:

transcriptic projects

                                   PROJECTS:

              PROJECT NAME              |               PROJECT ID
--------------------------------------------------------------------------------
PCR                                     |             p18qua34567db
--------------------------------------------------------------------------------
Directed Evolution                      |             p18qrn9745vfz
--------------------------------------------------------------------------------
I'll come up with a name later          |             p18s63543jm9t3
--------------------------------------------------------------------------------
Bad Blood... work up                    |             p18qupn345v99
--------------------------------------------------------------------------------
Red... Fluorescent protein cloning      |             p18qrjd345u89
--------------------------------------------------------------------------------

python growth_protocol.py | transcriptic submit -p p18qrjd345u89

Run created: https://secure.transcriptic.com/swiftpharma/p18qrjd345u89/runs/r18sc542345

And that is the run submitted and the robots will execute it.

After the run completes the data can be downloaded from the web app as a CSV or via the API. I will cover data analysis in another post.

If you have any questions head to the forum to further reading check out the Transcriptic support site at developers.transcriptic.com

Transcriptic will be at SLAS2016 on booth #1423

So I’m going to let you in on a little secret... Transcriptic is going to be at SLAS 2016 and we’ve got some amazing things to share with you. SLAS is the Society for Laboratory Automation and Screening, and SLAS2016 is the annual conference perfect for those seeking automated tools to conduct their research, I think we’ll fit in pretty well.

We’re going to be there with a workshop, 4 posters and an amazing booth (#1423) that we would love for you to visit. Let’s kick off with a fan favorite.

We’re bringing CRISPR tools with us!

That’s right, Dr. Jim Culver, one of the wonderful team at Transcriptic will be presenting the new automated workflow for CRISPR constructs built on top of the Transcriptic robotic cloud lab. Jim’s poster is entitled Assembling CRISPR gRNA Constructs Using the Transcriptic Robotic Cloud Laboratory. Jim’s poster describes the assembly of gene editing CRISPR constructs produced remotely in Transcriptic’s cloud laboratory. Transcriptic was used to achieve a 100% success rate for the assembly and transformation of bacteria with each CRISPR construct design.

 

So if you’re using, or thinking about using CRISPR it’s a must see presentation. You can find Jim’s poster in the Automation & High Throughput Technologies category on Monday Jan 25, 1:00 PM - 3:00 PM, poster number #3037.

Repeatable site directed mutagenesis all from your laptop, without touching a single pipette.

Dr. Yin He from Transcriptic, equally as wonderful as Jim, is presenting her poster on Kunkel mutagenesis. The poster, entitled Use of the Transcriptic Robotic Cloud Lab for High-Throughput Site-Directed Mutagenesis describes the ease of performing automated site-directed mutagenesis at scale with internet and the Transcriptic robotic cloud lab. 32 mutants were designed and successfully transformed into bacterial hosts.

Dr He’s poster will be in the Automation & High Throughput Technologies category on Monday Jan 25, 1:00 PM - 3:00 PM, poster number #3028, find out how you can exploit the power of Kunkel mutagenesis at scale.

Application of Autoprotocol for the critical assessment of liquid handler reliability

Autoprotocol, the popular open standard for documenting protocols was used by Dr. Peter Lee in the study of a variety of liquid handlers. Peter’s poster, The Transcriptic On-Demand Robotic Cloud Lab Reliably Performs High Throughput qPCR, demonstrated the flexibility and critically, the benefits of designing experimental protocols adherent to the Autoprotocol data standard. An Autoprotocol adherent qPCR protocol was used to test the reliability of a selection of three commercially available liquid handlers, in a highly repeatable way.

Dr Lee’s poster can be found in the Automation & High Throughput Technologies category on Monday Jan 25, 1:00 PM - 3:00 PM, poster number #3039.

High throughput drug screening on PDCs via the internet with Transcriptic.

You weren’t expecting that heading were you! Alyssia Oh et al. from CPMC are presenting High Throughput Precision Drug Screening of Patient Derived Tumor Cells on Transcriptic's Cloud Based Laboratory. In this poster the authors describe their method for high throughput screening of anticancer drugs on patient derived cells (PDC) from patient derived xenografts (PDX). This amazing high throughput screening was conducted on Transcriptic’s robotic cloud lab. 

You can find the poster in the Screening and Assay development category on Monday Jan 25, 1:00 PM  - 3:00 PM, poster number #2169

Last but not least, our Transcriptic workshop

Dr Conny Scheitz will be running a workshop for the attendees of SLAS2016 on how to integrate Transcriptic in their work flow to reap the benefits of our robotic cloud lab. The workshop will be covering how to launch protocols from our standard library of protocols straight from your laptop. Be guided through Transcriptic at our workshop on January 26, at 9:30am in room 11B.

We really do have a fantastic selection of presentations on the future of biology I do hope you will come and say hello.

Welcoming the HIG to our robotic cloud lab

I feel the need… the need for speed.
— Maverick & Goose, TopGun, 1986, (five stars)

A quick update for all of our wonderful Transcriptic users. We recently added the Bionex HiG microplate centrifuge into to our robotic cloud lab expanding our centrifuge capabilities. The HiG is a great piece of kit and we’re very happy to have it.

So what does this mean? 

OK, well before this you could only spin up to 1000G, but now the limit has been increased to 4000G!! This is awesome, it means more efficient centrifugation for pelleting cells, minipreps, performing separations and anything else our clever users come up with.

For our users that launch protocols from the protocol browser this means better performance of your protocols and for our developers creating your own protocols and experiments with www.autoprotocol.org this means you have an increased parameter space to play with no changes to the data structure you are used to.

A quick Autoprotocol refresher for the `spin` operation.

I’m sure you don’t need it, but here it is just in case. Below is a small JSON snippet of Autoprotocol generated with some of the new variables for the parameters. Acceleration can be specified in terms of “g” or “meter/second^2” if you’re not a fan of the gravitational constant...

{
  "op": "spin",
  "object": plate,
  "acceleration": "4000:g",
  "duration": "120:second"
}

As always, for the full description of capabilities see the documentation https://developers.transcriptic.com/docs/centrifugation and for questions jump on the community forum at https://forum.transcriptic.com.

Till next time, happy experimenting.

Ben and the team at Transcriptic

Welcome Marie, Michael and Tom!

Transcriptic has grown fast over the last 8 months. In fact, we've doubled in size since we closed our $9M Series A back in January:

It's amazing how things that worked well back when we were eight people don't work at all now, and how much the feeling of the company has changed over time.  This growth meant that we had to start thinking about how to go from engineering a technology to building a company.

The key to this for us was having the right leadership in place. Over the last three months we've welcomed three important new leaders to the company, who have made a night-and-day difference for us.

Tom Driscoll is our new VP of Business Development. Tom’s been around the block and brings deep commercial experience as principal of his own consulting business, VP of Marketing and Business Development at Fluxion Biosciences, VP of Global Marketing at Molecular Devices, VP of the Bioimaging business at Becton Dickinson, and Director of Marketing at Clontech.

That's a pretty serious resume, and we're very happy to have him on board.

Michael Lin is our new VP of Operations. Before joining us, he was responsible for business continuity as part of the senior operations team at Invitae. Before Invitae, he ran the assay product group at Fluidigm and built it from negligible revenue to millions per year.  At Transcriptic, he has overall responsibility for lab operations. Michael is the principal guardian of our efficiency and quality metrics.

Marie Lee is our new VP of Applications. Her job is to make sure that customers are successful and that we're able to quickly and efficiently onboard new assays and methods.  She's the interface between operations, engineering and business development that ties everything together. Before Transcriptic, she was a Senior Applications Scientist at Fluidigm, where she played a very cross-functional role between sales, marketing and R&D. Before Fluidigm she was a Field Application Scientist at Thermo-Fisher. She's taught at USF as an Adjunct Professor, and did a postdoc at UCSF.

I'm very lucky to get to work with such a world-class team, and we're still growing fast. If you're interested, check out our open positions and get in touch!  Thanks to IA Ventures, Data Collective, Google Ventures, and all of our other investors for having the confidence to back us before any of this was together, and of course our customers who have entrusted their science to a lab they can't see or touch.  We're excited to be doing what we do.

Provisioning Commercial Reagents

Over the last few weeks we’ve made some big changes to our inventory reservation system: most notably the "reserve" button next to each reagent that allowed you to reserve an aliquot and make it available in your inventory has disappeared. In the interest of allowing the reservation of arbitrary amounts of resources instead of pre-designated aliquot sizes, we’ve switched from a system of reserving resources to provisioning them. This way, you only pay for the reagents you use and Transcriptic takes care of making sure reagents are as fresh as possible so you don’t have to. For most users, this transition doesn’t mean much except less work. For protocols where you would have had to choose aliquots of reagents like ligase buffer or polymerase that you had previously reserved from your own inventory, appropriate volumes of those reagents are now automatically provisioned from within the protocol and pricing is rolled into the cost of the run accordingly. 

For developers submitting custom protocols, this change means switching over to using the provision instruction within scripts as you would a transfer (with some special considerations, read below). Resource IDs for use in the provision instruction can now be found by clicking on a given resource within the catalog.

The most appropriate way to use provision is to include as few provision instructions for a resource within a protocol. Calculate the total volume of each reagent you’ll need for the protocol you’re writing and provision that amount into the appropriate container type(s). Be sure to keep our design considerations and container dead volumes in mind as you do this. Additionally, consider the storage condition of the container you’re provisioning a resource into if you don’t plan to discard it after a run. For example, if you would like to transfer 5ul of water into every well of a 96 well plate, provision at least 495uL of water into a new tube and distribute or transfer it from there. Do not provision 5ul from the common stock into each of your 96 wells. Provisioning once will decrease freeze/thaw cycles and preserve Transcriptic's common stock.

There are also several limitations for developers using provision within their scripts:

  • the minimum volume you can provision of a given resource is 2 microliters (with the maximum being the maximum volume of the container you’re provisioning into)
  • you may only provision a resource into a maximum of 12 wells per provision instruction
  • a maximum of 3 provision instructions specifying the same resource should exist within one protocol

One place you can’t use the new provision instruction (for now) is for 6- and 1-well agar-filled plates. These are the only resources that still use the reservation system instead of provision until we extend this feature for use with solid resources.

In the meantime, the following code can be imported to your script to reserve plates with agar and the antibiotic of your choice:

from autoprotocol.container import Container
from autoprotocol.protocol import Ref

def ref_kit_container(protocol, name, container, kit_id, discard=True, store=None):
    kit_item = Container(None, protocol.container_type(container))
    if store:
        protocol.refs[name] = Ref(name, {"reserve": kit_id, "store": {"where": store}}, kit_item)
    else:
        protocol.refs[name] = Ref(name, {"reserve": kit_id, "discard": discard}, kit_item)
    return(kit_item)


def return_agar_plates(wells):
    '''
        Dicts of all plates available that can be purchased.
    '''
    if wells == 6:
        plates = {"lb-broth-50ug-ml-kan": "ki17rs7j799zc2",
                  "lb-broth-100ug-ml-amp": "ki17sbb845ssx9",
                  "lb-broth-100ug-ml-specto": "ki17sbb9r7jf98",
                  "lb-broth-100ug-ml-cm": "ki17urn3gg8tmj",
                  "noAB": "ki17reefwqq3sq"}
    elif wells == 1:
        plates = {"lb-broth-50ug-ml-kan": "ki17t8j7kkzc4g",
                  "lb-broth-100ug-ml-amp": "ki17t8jcebshtr",
                  "lb-broth-100ug-ml-specto": "ki17t8jaa96pw3",
                  "lb-broth-100ug-ml-cm": "ki17urn592xejq",
                  "noAB": "ki17t8jejbea4z"}
    else:
        raise ValueError("Wells has to be an integer, either 1 or 6")
    return(plates)

Example Usage:

import json
from autoprotocol.protocol import Protocol
# assuming you've pasted the above helper code into a file called reserve_plates.py:
from reserve_plates import *

protocol = Protocol()

z10b = protocol.ref("Zymo10B", None, "micro-1.5", discard=True)
# provision bacteria
protocol.provision("rs16pbjc4r7vvz", z10b.well(0), "50:microliter")
# dilute with LB
protocol.provision("rs17bafcbmyrmh", z10b.well(0), "350:microliter")
protocol.mix(z10b.well(0), "150:microliter")
myplate = ref_kit_container(protocol,
                            'my_agar_plate',
                            '6-flat',
                            return_agar_plates(6)['noAB'],
                            store='cold_4')
for i in range(0,6):
    protocol.spread(z10b.well(0), myplate.well(i), "60:microliter")
protocol.cover(myplate)
protocol.incubate(myplate, "warm_37", "16:hour")

print json.dumps(protocol.as_dict(), indent=2)

Adding potential energy: Transcriptic's Series A

Today I'm excited to announce that we've raised approximately $8.5 million in a Series A financing, bringing the total investment in the company to a little over $14 million. The round was led by Data Collective with participation from IA Ventures, AME Cloud Ventures, Silicon Valley Bank, 500 Startups, MITS Fund, Y Combinator, Paul Buchheit, and a bunch of other angels. The round officially closed at the very end of December, 11 months after we raised a $2.8M "Series Seed" led by IA Ventures.

As Dalton Caldwell of YC likes to say, raising money is like having gas in the tank of your car: it gives you potential, but you haven't actually gone anywhere yet. It's very important not to confuse fundraising with actual success.

In the last month alone we've released a new way to launch protocols via the web, an easier way to buy commercial kits directly through your Transcriptic account, split off our protocol language, Autoprotocol, into an open-source project, and completely overhauled our documentation in addition to adding hundreds of minor features and bugfixes throughout the Transcriptic experience. We're now almost 30 people and have lab ops running around the clock on most days.

When we started Transcriptic, we set out with the goal of giving the life sciences the same structural advantages that web has enjoyed, making it possible for two postdocs with a laptop in a coffee shop to run a drug company without the need for millions of dollars in capital equipment or lab space. To be clear, we are not there yet. However, with an incredible team and set of investors and partners, we are now in the rare and fortunate position of being able to take a real "shot on goal" on a truly large and interesting problem.

We're currently hiring for a bunch of positions. Specifically, if your're either a:

you should definitely get in touch.

Buying Reagents Through Transcriptic

Your own lab is stocked full of the commercially-available reagents and kits you use every day. But when you want to use those same reagents on Transcriptic, until now you've had to aliquot them out by hand and ship them individually (if your organization even allows it!)

Today that process is becoming much, much easier. Just click on the 'Catalog' tab in your inventory and type in the name of the reagent or kit you're looking for, and reserve as much as you need, and it will be instantly available in your inventory for use. In most cases, it's actually cheaper to buy reagents through Transcriptic than it would be to buy them directly from a vendor—instead of having to purchase a $1,000 kit with 500 reactions, you can just buy the amount you need, when you need it.

If your experiment uses only synthesized DNA and commercially-available reagents, you can run it completely on Transcriptic without ever having to touch a pipette.

We take care of purchasing, storage and sample tracking, so you can be sure you'll never get an expired reagent or an enzyme that a grad student left on the bench for two hours last week. Every tube and aliquot you purchase through Transcriptic is marked with its lot number, expiration date, and volume.

We hope this gets you one step closer to being able to put down that pipette for good, and run better, more reproducible experiments.

As always, we'd love to hear your thoughts and feedback—get in touch!

An Easier Way To Launch Protocols

The Transcriptic platform is a reliable, repeatable and extremely flexible tool for running biology experiments. But until now, the only way to take advantage of that power has been to write code.

Today we're launching an all-new, easy-to-use interface for browsing and executing pre-written protocols. No longer will you need to understand JSON and POST requests to start a qPCR reaction: just find an appropriate protocol from the repository, fill in a few fields and click "Launch". It couldn't be simpler.

These protocols are built on top of the Autoprotocol open standard, meaning they can be shared, reused and built upon. In fact, we've already written and released a Core Library of standard protocols. Protocols are versioned, so you're guaranteed that a protocol won't change unexpectedly from one execution to the next. When you want to repeat an experiment, you can be sure you're getting exactly the same protocol.

With a little Python knowledge, you can easily create and upload your own protocols for use on Transcriptic. We've written a short tutorial to get you up and running with Autoprotocol and quickly. (Of course, there's nothing special about Python—any language can generate Autoprotocol!)

While Transcriptic is still a fairly technical system, this system makes it dramatically easier to use, and removes the need to program to get started. Coupled with the Autoprotocol Standardautoprotocol-python library and Autoprotocol Core Protocols it's easy to start with the pre-built protocols available and switch to more powerful tools once you become productive with the basics and start yearning for greater flexibility.

We're eager to hear your feedback of what you think and how it works for you. Try it out, and don't be afraid to get in touch!

The Autoprotocol Language Standard

Expressing protocols for biological research in a way that is both human and computer-readable is a fundamental prerequisite for Transcriptic's cloud laboratory model. This is a topic we've written about before and our basic answer has been described in great technical detail in our Low-Level API documentation for some time.

Over the last several months we’ve revised and further developed the language we use to encode biological protocols to be run on our robotics and are proud to formally release it as an open standard we’ve named Autoprotocol.

Unlike many of the other formal protocol languages out there, only Autoprotocol is:

  • useful today: if you implement a method for Autoprotocol using supported instructions, you can run it right now on Transcriptic and get real data back,
  • fully synthesizable, meaning exactly zero human interpretation is necessary,
  • fully schedulable, meaning you can know ahead of time what will run and when for a given protocol,
  • and now, open source.

Today we're excited to announce that we've published a separate, independent website detailing Autoprotocol's semantics under autoprotocol.org. Autoprotocol is an open standard that anyone can use to express and share protocols intended to be read by both laboratory automation machinery and humans. We hope to contribute to better science by providing an unambiguous, structured language for precisely describing protocols and by providing a platform for scientists to collaborate and iterate on those protocols. Expressing and sharing protocols in this way allows for more reproducible experiments that produce more meaningful results. The Autoprotocol standard itself is open to contributions and improvements from the community. Come collaborate with other forward-thinking biologists who are embracing the future of scientific research!

But wait, there’s more! If the structure of Autoprotocol looks intimidating to you, never fear. Today we’ve also released a Python library to make it easy to generate protocols with Autoprotocol. With this comes Autoprotocol Core, a library of standard protocols in order to provide examples to follow and allow you to avoid the need to write any code at all for some common use cases.

YC + Transcriptic

Today we're extremely excited to announce a new partnership with Y Combinator to help get a new generation of lean biotech companies off the ground.

Long story short, we're offering $20,000 in Transcriptic Platform credits to all YC biotech companies (past, present and forseeable future) to help them test out their ideas faster without needing to invest in any equipment or spend any time building out a lab. We'll make our Implementation Scientists available to all YC companies to help them think through and design their experimental protocols and ensure that they're able to be successful on Transcriptic.

Separately, YC is also investing in us. Even though we've already raised nearly $6 million and are already 18 people, the YC network is hands down the best community I've seen in my time in Silicon Valley and we're very excited about becoming part of it. We're hoping that this is the start of a long and interesting relationship, of which the Platform credits deal is just the beginning.

There's never been a better time to start a biotech company, and as YC grows and evolves it's making biotech a big new focus. If you're considering starting a company, it's not too late to apply late for the upcoming Winter 2015 batch.

The LIMS Data Model: Sample Management

I spend a lot of my time now going out and talking to researchers - academia, nonprofits, biotechs, pharmaceuticals - and one of the questions I always ask, whatever else we're talking about, is what kind of lab or inventory management system they use. The response is nearly always: "my memory, or Excel, maybe."

A curious state of affairs.

Why aren't people using inventory management systems? Overwhelmingly it seems that labs just aren't keeping track of the samples they generate. No one really knows what it's in the tubes left by that postdoc that have been in the back of the freezer for the last year. Industry users do have LIMS systems, but they're heavily reliant on data entry and despite being described in glowing terms like "mandatory" they aren't actually used very much in reality. Plates of high value samples are typically tagged only for long-term storage, breaking a "chain of custody" of what actually happened to the sample all the way through the process. Exceedingly few people update the central database after every pipetting operation.

One of the nice side-effects of Transcriptic Platform is that you get all this logging completely for free. There's no data entry to do: each Run automatically updates the sample management system as it executes. This makes it possible to reconstruct the exact lineage of every aliquot of every resource back to the original vendor orders and new sample check-ins from collaborators.

LIMS - "lab information management system" - software has been around almost as long as computers but has remained expensive and complicated. Many big companies build their own solutions in-house rather than wrestle with one of the commercial packages. Typical LIMS solutions are sufficiently convoluted that the website of one of the largest commercial systems, Core LIMS, features exactly zero screenshots of their web-based product as far as I can tell, which one can't sign up for directly. The implication is that it's so "configurable, flexible, [and] extensible" that any concete screenshots wouldn't be relevant for your application.

This mode of thinking delivers solutions with lots of complexity to justify the high costs that require extensive customer-specific modification. I believe that it's a sign that the traditional LIMS data model is wrong.

There are two halves to the sample management question, each of which has historically been addressed separately. My hypothesis is that these two sides must reconciled in order to get a sufficiently useful LIMS system.

The first half are the "aliquots": you have 32 μl of something in a 1.5 ml Eppendorf tube in freezer 37, full stop.

The second half are the "resources": pSB18-D7 is a plasmid containing four genes, four promoters, an origin of replication, and some other sequences.

The linkage between resources and aliquots is critical but surprisingly subtle. There doesn't seem to be any solution on the market today that does it well. Many of the commercial solutions allow tracing resource lineage—this cell line is the result of transfecting this other cell line with this virus—but then they annotate this information onto a sample and treat it as permanent, which isn't true.

The core of the problem lies in the fact that the linkage is time-varying and probabilistic. If you buy a tube of TOP10 Competent Cells from Life Technologies today, the cells are probably alive and competent. As time goes on, these things become hazier. Three weeks from now, after the door to the freezer has opened and closed a hundred times, what's the probability that they've retained competency? 80%? 70%? The resource-aliquot link decays over time.

The resource-aliquot linkage is probabilistic

When an aliquot is associated with a resource, the probability that the linkage is current momentarily jumps to 100%. Immediately thereafter it starts to decay: as time goes on there's a risk that the biological identity could have shifted. When an aliquot is operated on in the lab, the probability that it still retains its last resource identity typically plummets. Asserting a new resource linkage restores high confidence to the biological identity of the aliquot. Passive decay can happen at varying rates: for example, an aliquot of a small molecule in DMSO is less likely to have changed after two weeks compared to an aliquot of an engineered bacteria.

Take bacterial transformation as another example: you start with two aliquots, one of "bacterial cell" resource type and one of "DNA" resource type and end with... a question mark. Did the transformation succeed and did the cells take up the DNA? In the figure above, after the instructions are applied the resource identify of aq1 is really unknown. Some analytical method must be applied to know whether annotating r2 onto it is the right thing to do. The biological content matters and bringing that context into the protocol execution results is key.

Transcriptic makes this data model explicit by making aliquots, resources and the relationships between them equal first-class citizens in your inventory database. You can see for a given aliquot the last resource association as well as the relationship's age in terms of instruction depth—how much has happened to this aliquot since we last gave it an identity—and time. This context, paired with the automatic lab notebook generated by each run, means you have a lot more information about your experiments and samples than you've ever had before, all kept automatically up to date.

This kind of lab-linked informatics lets you move much faster and discover more effectively than was ever possible before. If you're interested in learning more about how Transcriptic can help your research program achieve its goals sooner, get in touch!

The Haven Facility

For the last twenty-six (!) months Transcriptic has been in a small, 2,100-square-foot lab on Edison Way in Menlo Park, California. Three weeks ago we completed construction on a new 10,000-square-foot facility that we're pretty excited about.

The new lab, at 3565 Haven Avenue in Menlo Park, gives us space for up to 40 people; 800 square feet of human lab space that includes a tissue culture suite; a rapid prototyping lab; and room for many new robotic work cells.

Could probably use some more green leafy things.

Could probably use some more green leafy things.

With our brokers Eric Bluestein and Rico Cheung at Kidder Mathews we toured what felt like several dozen locations over a three month period last spring. We originally wanted to end up in South San Francisco: we lost a few really good candidates early on who didn't want to commute down from the city, and we felt like South SF was more of an epicenter of life sciences than the South Bay. Eventually we decided to stay in Menlo Park, and the reasoning was (roughly):

  • South San Francisco was still a commute from San Francisco. Once you were on the Caltrain, the perception was that you might as well make it down to Redwood City. The people we were losing because of the commute didn't really want to leave SOMA, and while being in the city would be nice it was simply cost prohibitive for us to build a lab there.
  • With that in mind, South SF began to feel like a compromise that pleased nobody. The people who lived around Palo Alto would now have a real commute, too, in addition to the people who lived in SF.
  • It was a lot more expensive. We were seeing prices near $4 per square foot in South SF compared to under $2.50 psft further south.

In the end, it was really the feeling of Haven that pushed us over the edge compared to the other finalist spaces we were considering. The high, exposed ceilings and concrete floors felt like a place we wanted to work. It met all of the technical requirements (more on those later) given our budget.

Workcells go here.

Workcells go here.

We signed the lease in March. We moved in September. I'll take "things that will take way longer than you expect" for $300, Trebek. In fairness, this was almost exactly as long as the brokers told us it would take.

One of the reasons this happened is that we did a lot of construction. We needed a facility with a machine shop, wet lab space including a clean room for tissue culture, and special electrical and HVAC work for our robotics, among other things.

In the photo above you can see an exhaust plenum with six ports, one to connect up to each robotic unit that will eventually fill that open space. Haven is a fairly specialized office, tailored to a fairly unusual company. All told, we spent over $175,000 on the construction. The biology lab was completely new: the wall that the exhaust ducting is mounted on above didn't exist before.

entryway.jpg
Many Transcriptics bike into the office; Caltraining down from SF and riding from the station is common.

Many Transcriptics bike into the office; Caltraining down from SF and riding from the station is common.

A robotic workcell undergoes testing.

A robotic workcell undergoes testing.

Interestingly, the first thing we'll probably outgrow is power. Enough power was a critical requirement for us when we looked for our new building and ruled out a bunch of otherwise good options for us early on. We budget our lab pessimistically for a somewhat remarkable amount of electricity. (As an aside, the grid is surprisingly unreliable. Thinking about how we fail over electricity to generators or batteries, or in the case of freezers, compressed CO2, becomes a big deal.)

electricity.jpg

Exhibit A: Not enough electricity

Haven only has 800 amps, which becomes fairly limiting once you start stacking thermocyclers and mass specs, and adding support infrastructure like air compressors and vacuum pumps.

Adding electricity to a building is hugely expensive. It can be high five-figures or more to bring more power into a building from a nearby conduit, and requires upgrading panels throughout the building. Just adding a new 400-amp panel once the power has been brought into the building can cost upwards of $10,000.

In addition to our robotics, we also have a manual lab of our own. There are processes that aren't automated yet, and we also do a lot of internal development and debugging of our workflow. We have a dedicated team of staff scientists who focus solely on supporting customer runs and making sure that everything works for them.

It goes without saying that having our own real facility like this is incredibly exciting. We went from being stuffed into an office that was very borderline on qualifying as a literal garage to a heavily customized large part of an entire building.

You'll notice that not shown in this post was much of a view of the work cells themselves. We'll be showing aspects of our custom hardware off in subsequent posts in detail. Stay tuned!

Announcing Transcriptic Platform

Today we're releasing Transcriptic Platform, an API for wet-lab experimentation. This is a culmination of over two years of work thinking hard about what biological research is and should be, coupled with big investements in software, manual process development, and robotic automation.

More than the automation, Transcriptic Platform is about seeing the world of research in a fundamentally different way than most researchers are used to. "What protocols can you run?" is almost always one of the first questions potential customers always ask us when we meet them. That question comes from a worldview where biology as a field struggles with reproducibility and lab heads discuss having "soft hands" as a reasonable explanation for why some technicians have greater success than others. We see the world differently.

Machine-readable protocols have a long history. Some of the most interesting previous work includes Microsoft Research's BioCoderCIDAR PuppeteerAquariumCOW, and EXACT. There was even briefly a short-lived Automated Experimentation journal dedicated partly to the topic. Capturing biological intent at a high-level in a way that's guaranteed to be precise and unambiguous while remaining flexible and composable is a difficult design challenge. Going from that to something "synthesizable"—mappable directly onto hardware—is the subject of a whole field of study.

BioCoder was one of the first big papers to address the challenge directly. Its modular design with pluggable backends capable of generating natural language text, Graphviz, or (hypothetically) instructions for automation was based on traditional modular compiler design and influenced how future groups approached the problem. Ultimately it was severely hindered by its reliance on string descriptions, making it more of a document formatting system than a machine-readable protocol language—a domain-specific LaTeX. The unfriendliness of C++ and the misleading appearance of Turing-completeness—an if statement doesn't have the same effect in a code generator as it does in normal program logic—were also limiting.

The first major difference between Transcriptic and earlier work like BioCoder is the simplifying realization that a protocol is just data. While "real" protocols often include many dependent steps, Transcriptic protocols contain no branching logic. They can generate data and you can make decisions based on this data, but that is totally external to the system. Because of this, protocols can be simply specified without a need to decide which programming language to build on (C++? Ruby? Python?) or invent a new one. Transcriptic protocols are encoded as JSON and can be produced by anything and consumed by anything, and don't require a virtual machine or compiler to interpret.

One of the other key insights here is that a vast swath of biological research—80% to 90% or more—is covered by less than 15 distinct devices. Even though there's a huge range of "protocols", that diversity mostly relies on liquid handling, incubation, refrigeration, centrifugation, spectrophotometry, mass spectrometry, flow cytometry, gel separation, thermocycling, nucleic acid sequencing, and liquid chromatography, in addition to a small constellation of minor accessory devices like plate sealers and cappers and decappers. There are differences within some of these categories—is an Orbitrap or a triple quadrupole mass spec better for your application?—but these variations add only a constant factor to the device set and the fundamental premise remains the same.

Accordingly, Transcriptic Platform sees the world in terms of devices, not protocols. Each device exposes one or more instructions - a liquid handler can pipette, a centrifuge can spin, and a plate reader can take absorbance, fluorescence, or luminescence readings. A protocol is simply a composition of the capabilities of the devices in the lab.

So, what protocols can we run and what assays can we do? Well, the α-1 instruction set can be composed into nearly endless variations. Everything can be different - the idea of fixed protocols changes completely.

Protocols do need optimization for our environment. In this way we're more similar to a semiconductor foundry than the cloud computing metaphor we often use. When a customer chooses Transcriptic, they must follow a set of design rules specific to our platform. Some of these design rules are changes stemming from the unstructured nature of human labor - there's no way to specify "tap the tube until the solution changes color" using Transcriptic instructions. Others are related to important implementation details; automated liquid handling, in particular, is deceptively complex and can be very precise but requires special attention to become so.

The payoff is strongly reproducible work that's fully asynchronous from the perspective of the scientist.

With nearly $6 million in funding to date from groups like IA Ventures, Google Ventures, and Silicon Valley Bank we have a firm foundation to try and help scientists solve some of the hardest problems in research.

Our role in the community is a supporting one. We aren't the ones synthesizing knowledge into discoveries or bringing life-saving drugs to market. We are here to help you work better, but it's not about us. That's why, despite the fact that this is hands-down the most capable and accomplished team I've ever worked with, there's no page on the website with photos of us.

We're very excited to see what you build.