Shreejoy Tripathy Thesis Defense Talk

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Understanding the form and function of neuron diversity

Shreejoy TripathyCarnegie Mellon UniversityEmail: stripat3@gmail.com

Twitter: @neuronJoy

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Thesis overview

1. How are neurons within the same type different?

Tripathy et al 2013, PNAS

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Thesis overview

1. How are neurons within a type different?

2. How are neuron types throughout the brain different?

Tripathy et al, in preparation

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Thesis overview

1. How are neurons within a type different?

2. How are neuron types throughout the brain different?

3. How can we best utilize existing data and knowledge?

?

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Outline

• Background on neuron electrophysiology• The role of neuron variability• NeuroElectro: a window to the world’s

neurophysiology data• Analyzing the electrophysiological diversity of

neurons throughout the brain• Extending NeuroElectro and future directions

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Ways of looking at neurons: Neuron morphology

Ramon y Cajal, 1905

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Neuron electrophysiology

Membranevoltage

Time

Currentinjection

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Ion channel basis of electrophysiology

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Summarizing neuronal electrophysiological properties

Membranevoltage

Time

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Outline

• Background on neuron electrophysiology• The role of within-type neuron variability– How are olfactory bulb mitral cells different from

one-another?– What is the computational role of mitral cell

differences?

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Mitral cell variability

Krishnan PadmanabhanNow at Salk Institute

Olfactory bulb mitral cells(named because their shape looks like a bishop’s hat, or mitre)

• Individual mitral cells are biophysically variable from one another

Padmanabhan and Urban 2010, Nat Neuro

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Modeling and explaining the significance of mitral cell variability

• Captured the variability across mitral cells using statistical models– Models allowed for

precisely quantifying neuron differences

Tripathy et al 2013, PNAS

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Modeling and explaining the significance of mitral cell variability

• Captured the variability across mitral cells using statistical models

• Mitral cell populations optimally encode information when at an intermediate level of variability

Tripathy et al 2013, PNAS

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Outline

• Background on neuron electrophysiology• The role of neuron variability• NeuroElectro: a window to the world’s

neurophysiology data– Why do we need a neurophysiology database?– Live demo of web interface at neuroelectro.org– Brief methods explanation

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Extending neuron comparisons to other neuron types

• What makes mitral cells unique?

• Could I extend my results on mitral cells to other neuron types?– “Is a mitral cell more like a CA1

pyramidal cell or a cortical basket cell?”

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Extending neuron comparisons to other neuron types

• What makes mitral cells unique?

• Could I extend my results on mitral cells to other neuron types?– “Is a mitral cell more like a CA1

pyramidal cell or a cortical basket cell?”

Pubmed searches

Recording new data

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Extending neuron comparisons to other neuron types

• What makes mitral cells unique?

• Could I extend my results on mitral cells to other neuron types?– “Is a mitral cell more like a CA1

pyramidal cell or a cortical basket cell?”

Pubmed searches

Recording new data

The lack of a database on neuron types and their properties leads to:• Increased difficulty in comparing

neurons• Overall slower progress and more

narrow-minded focus in the field

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Availability of useful databases in genetics

Entreztools

CCCATTGCGCCAAGCCCGTT…

CCCATAGGGCCAAGTTCGTT…

97%; human Kv1.1 (KCNA1)

95%; mouse Kv1.1 (Kcna1)

Genetic deficit in gene coding for Kv1.1 potassium channel

CCCATTGCGCCTAGGGCGTT…

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NeuroElectro: text-mining neuron properties from the existing literature

Tripathy et al, in preparationinspired by Aaron Swartz, IBM’s Watson, neurosynth.org

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NeuroElectro web interface

• [demonstration of http://neuroelectro.org web interface]

Built with Rick GerkinNow at Arizona State

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Semi-automated literature-mining overview

• Simple algorithms that use simple text searching to identify: – Biophysical properties (in

normotypic conditions)– Neuron types (from

NeuroLex.org)– Biophysical data values– Methodological details

• Text-mined data is then checked by experts

>+

Tripathy et al, in preparation

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NeuroElectro: a window to the world’s neurophysiology data

• NeuroElectro: a database of neuron types and their biophysical properties in normotypic conditions– Built through literature text-mining + manual curation– Currently 100 neuron types, >300 articles

• Anyone can access, visualize, and download this data at neuroelectro.org

• Currently working with computational modelers to provide parameters for neuron models

• Future efforts will integrate raw data, data not from normotypic conditions including animal models of disease

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Outline

• Background on neuron electrophysiology• The role of neuron variability• NeuroElectro: a window to the world’s

neurophysiology data• Analyzing the electrophysiological diversity of

neurons throughout the brain– How do we reconcile data collected under different

experimental conditions?– What new things can we learn about functional

relationships between different neuron types?

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Example electrophysiological data

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How to deal with differences in experimental conditions?

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How to deal with differences in experimental conditions?

• A tale of (representative) 2 labsUrban Lab Barth Lab

“Slices were continuously superfused with oxygenated Ringer’s solution warmed to 37°C”

-Burton et al 2012

“Slices were maintained and whole-cell recordings were performed at room temperature”

-Wen et al 2013

Stated reason: “more physiological, more realistic results”

Stated reason: “healthier neurons”

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How to deal with differences in experimental conditions?

• Experimental condition differences are relevant to all experimental disciplines

• Addressing effect of differences in experimental conditions is generally quite difficult– “I only believe my data and noone elses”

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Example experimental conditions

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Example experimental conditions

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How to deal with differences in experimental conditions?

• Idea: use statistics to model the influence of experimental conditions (“metadata”) on electrophysiological measurements (“data”)

Spruston et al 1992Zhu et al 200

Electrode type

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Influence of specific experimental metadata

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Explaining measurement variance with experimental metadata

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Analyzing neuronal electrophysiological diversity

• How do we reconcile data collected under different experimental conditions?– Use statistics to learn the relationship between experimental

conditions and data measurements– “Adjust” experimental measurements to as if they were

collected under the same conditions– More experimental conditions (like recording solution contents)

can be extracted; improved metadata models in future• What new things can we learn about functional

relationships between different neuron types?– How are biophysical properties correlated?– Are there unknown similarities among neuron types?

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Exploring correlations among biophysical properties

τ = RC

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Exploring correlations among biophysical properties

3636

Neuron types

Neu

ron

clus

terin

g on

bas

is o

f el

ectr

ophy

siol

ogy

3737

Neu

ron

clus

terin

g on

bas

is o

f el

ectr

ophy

siol

ogy

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Neu

ron

clus

terin

g on

bas

is o

f el

ectr

ophy

siol

ogy

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Analyzing neuronal electrophysiological diversity

• What new things can we learn about functional relationships between different neuron types?– Electrophysiological properties are highly

correlated across neuron types– NeuroElectro provides a novel platform for

generating hypotheses on functional similarities of neuron types

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Outline

• Background on neuron electrophysiology• The role of neuron variability• NeuroElectro: a window to the world’s neurophysiology

data• Analyzing the electrophysiological diversity of neurons

throughout the brain• Extending NeuroElectro and future directions– Validating the data contained within NeuroElectro

• Obtaining data at higher resolution

– What is the mechanistic basis of neuron diversity?• Integration with Allen Institute Gene Expression Atlas

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Validating and extending NeuroElectro

Shawn Burton, CMU Biology

CA1, pyr

Ctx, pyr (L5)

MOB, mit

Ctx, bskt

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Validating and extending NeuroElectro

Shawn Burton, CMU Biology

CA1, pyr

Ctx, pyr (L5)

MOB, mit

Ctx, bskt

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Validating and extending NeuroElectro

Tripathy et al 2013de Waard et al 2013 in press

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Extending NeuroElectro and future directions

• Data collected using Urban Lab specific protocols is consistent with NeuroElectro data– Currently exploring how to integrate collected raw

data into NeuroElectro– NeuroElectro provides an easy check on data quality

• What is the mechanistic basis of neuron diversity?– Integration with Allen Institute Gene Expression

Atlas

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What is the mechanistic basis of neuron electro-diversity?

Central dogma of biology

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Whole-genome correlation of gene expression and electro-diversity

20,000 genes

Allen Gene Expression Atlas; Lein et al 2007

Systematic variation among

neuron types

Patterns of gene expresion

Electrophysiologicalphenotypes

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Mapping neuron electrophysiology to gene expression

20,000 genes

Neuron typeresolution

Cell layerresolution

Neuron type to cell layer mapping is approximate. Will be improved in future iterations with high resolution data.

Neocortex L5/6pyramidal cell

Neocortex layer 5/6

Neocortex basket cell

Neocortex

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Correlation of neuronal electrophysiology and gene expression

Electrophysiological differences

Pearson’s r = .36; p < 6*10-5

Neu

ron

type

s

Gene expression differences (voltage gated ion channel genes)

Cell

laye

rs/

brai

n re

gion

s

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Correlation of neuronal electrophysiology and gene expression

Functional gene classes from Gene Ontology projectAshburner et al 2000

not expressed in brain

ion channelgene classes

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Extending NeuroElectro and future directions

• Data collected using Urban Lab specific protocols is consistent with NeuroElectro data

• What is the mechanistic basis of neuron diversity?– Integrating NeuroElectro with the Allen Gene Expression Atlas

allows asking how gene expression influences neuron biophysics• Generates new hypotheses on link between specific gene classes and

neurophysiology

– Future gene expression datasets at higher cellular resolution will allow testing of more specific hypotheses

– Fleshing this out is a large part of my proposed work as a post-doc in Paul Pavlidis’s lab in Vancouver

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Summary/Take home message

• Neurons are different and differences underlie diversity of functions

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CA1,

pyr

Ctx,

pyr

(L5)

Why NeuroElectro?• We need a “parts list” of

the brain – BRAIN Initiative:

“consensus of cell types”

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Why NeuroElectro?

• How much “buried treasure” is in the literature?– What can we learn by

putting together what we already know?

20,000 genes

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Why NeuroElectro?

• The web lets us “share data” and ideas between researchers in a way that journal articles do not

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Acknowledgements• People

– Nathan Urban– Krishnan Padmanabhan– Rick Gerkin– Shawn Burton– Urban Lab (past and present)

• Institutions– Center for the Neural Basis of

Cognition– Allen Institute for Brain Science– Neuroscience Information

Framework– Elsevier Research Data Services– International Neuroinformatics

Coordinating Facility– Open Source Software Community

• Funding – NSF Graduate Research Fellowship– RK Mellon Foundation

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Summary of NeuroElectro contents

80 neuron types; 300+ articles

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Cross-validation for metadata correction

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Robustness of metadata correction analysis

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Projecting neurons onto low dimensional space

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Robustness of electrophysiological neuron similarity analysis

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Top 30 most biophysically correlated gene classes

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Big science versus small hypothesis-driven science

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Mechanistic explanations for neuronal biophysics

• The traditional approach:

Iberiotoxin, a BK ion channel blockerExtracted from the Indian Red Scorpion

Pharmacological drug

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Defining neuron variability

Padmanabhan and Urban, Nat. Neuro. 2010

Krishnan PadmanabhanNow at Salk Institute

Olfactory bulb mitral cells(named because their shape looks like a bishop’s hat, or mitre)

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Defining neuron variability

1. Define a precise way of quantifying neuron electrophysiology differences

2. What is the computational role of electrophysiology differences?

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The olfactory neural circuit

Volatile odor molecules

Nasal epithelium

Olfactory receptor neurons

Olfactory Bulb Mitral Cells To cortex

~25-50 per glomerulus

Olfactory Bulb

Glomeruli

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Neuron modeling approach

Neuron input (current)

Neuron output(action potentials)

A “model what you see” based approach

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Modeling mitral cell responses

Tripathy et al, in review

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Modeling mitral cell responses

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Models capture mitral cell biophysical variability

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Dimensionality reduction for visualization

Dimensionality Reduction(via PCA)

Neuron dimension 1N

euro

n di

men

sion

2

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Dimensionality reduction for visualization

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Dimensionality reduction for visualization

1. Define a precise way of quantifying neuron electrophysiology differences

2. What is the computational role of electrophysiology differences?

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Computational role of neuron variability: Approach

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Computational role of neuron variability: Key results

Tripathy et al, in review

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Importance of specific metadata

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Central dogma of molecular biology

Lots of great tools for data sharing…

Barriers to data sharing

• Social– “What’s in it for me? How will I get credit?”– “It’s my data, not yours”– “The benefit to me isn’t worth the time I put into it”– “What if I get scooped?”

• Methodological– “How do I share data? What do I share?”– “Going back and annotating my files to share is super-time

consuming”– Specifying file formats, data standards – Building FTP servers and nice user interfaces

Project idea

• How can we make a standard neuroscience wet lab more data-sharing savvy?

• Incorporate structured workflows into the daily practice of a typical electrophysiology lab (the Urban Lab at CMU)– What does it take?– Where are points of conflict?

Key insights/motivations

1. Effective data sharing includes raw data files + experimental metadata (typically stored in a lab notebook)

SDB_MC_12_voltages.mat

Key insights/motivations

1. Share raw data files + experimental metadata

2. You know the most about an experiment when you’re performing it

Key insights/motivations

1. Share raw data files + experimental metadata

2. You know the most about an experiment when you’re performing it

3. Improved data practices should make labs more productive

Project schematic

Metadata data app

• Electronic lab notebook models sequential slice-electrophysiology workflow – Replaces pen-and-

paper lab notebook

Metadata data entry

• Electronic lab notebook allows structured data entry

Animal Strain

Metadata data entry

• Electronic lab notebook allows structured data entry (i.e., dropdown menus)– Allows incorporation

of semantic ontologies

• Important to strike a balance between structure and flexibility

MGI:3719486

Metadata data entry

MGI:3719486

• Electronic lab notebook facilitates entry of new content, like registration of recorded neurons to brain atlas

Data integration• Syncing of metadata

app and electrophysiology data acquisition via server– Each trace of

experimental data annotated with metadata

• IGOR-Pro specific, support pClamp, other acquisition packages as needed later

Data dashboard (web-based)

Data dashboard (future-steps)

• Use collected metadata to sort experiments– Like mouse strain,

neuron type, animal age

• Enable in-browser analyses– Track provenance

of analyzed data back to raw data

Next steps

• Use built tools – Populate data server with many experiments

• Is use of e-notebook too prohibitive?– If yes, continue to iterate

– What can we ask now that we couldn’t before?• It is much easier to ask exploratory questions, like

– How is the cell type that Shawn records different from the one that Matt records?

• Exposing data to neuroscience databases– NIF, INCF Dataspace, neuroelectro.org

• How adaptable are these solutions for use in other labs?• Who is going to pay for this?