IN , SECONDDEGREE PROJECT MACHINE LEARNING 120 CREDITSCYCLE

, STOCKHOLM SWEDEN 2016

Classification of NeuronalSubtypes in the Striatum and theEffect of Neuronal Heterogeneityon the Activity Dynamics

BO BEKKOUCHE

KTH ROYAL INSTITUTE OF TECHNOLOGY

SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION

Classification of Neuronal Subtypes in theStriatum and the Effect of Neuronal

Heterogeneity on the Activity Dynamics

BEKKOUCHE BO

Master’s Thesis at CSCSupervisor: Hjerling-Leffler Jens & Kumar Arvind

Examiner: Lansner Anders2016-02-16

Abstract

Clustering of single-cell RNA sequencing data is often used to showwhat states and subtypes cells have. Using this technique, striatal cellswere clustered into subtypes using different clustering algorithms. Pre-viously known subtypes were confirmed and new subtypes were found.One of them is a third medium spiny neuron subtype.

Using the observed heterogeneity, as a second task, this project ques-tions whether or not differences in individual neurons have an impacton the network dynamics. By clustering spiking activity from a neu-ral network model, inconclusive results were found. Both algorithmsindicating low heterogeneity, but by altering the quantity of a subtypebetween a low and high number, and clustering the network activity ineach case, results indicate that there is an increase in the heterogeneity.

This project shows a list of potential striatal subtypes and givesreasons to keep giving attention to biologically observed heterogeneity.

ReferatKlassificering av neuronala subtyper i striatum och

effekten av neuronal heterogenitet påaktivitetsdynamiken

Klustring av singelcell RNA sekvenseringsdata används ofta till att visavilka tillstånd eller subtyper som celler har. Med denna teknik har härstriatala celler klustrats in i subtyper med olika klustringsalgoritmer.Tidigare kända subtyper har konfirmerats och nya subtyper har hittats.En av dem är en tredje medium spiny neuron.

Som en andra uppgift, med denna observerade heterogenitet somgrund, ifrågasätter detta projekt om skillnader mellan individuella nerv-celler har en inverkan pånätvärksdynamiken. Genom att klustra spik-aktivitet från en neural-nätverksmodell har tvetydiga resultat hittats.Båda algoritmerna visar på låg heterogenitet, men genom att ändra för-delningen av en subtyp och klustra i båda fallen så fanns resultat somindikerar att heterogeniteten ökar när antalet nervceller av subtypenökar.

Det här projektet visar en lista på potentiella striatala subtyperoch ger anledningar till att fortsätta ge uppmärksamhet till biologisktobserverad heterogenitet.

Contents

1 Introduction 11.1 The QUESTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Task 1. Classification of neuronal types in the striatum . . . . . . . 2

1.2.1 Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Task 2. Effect of neuronal heterogeneity on the activity dynamics ofthe striatum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3.1 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.5 Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.6 Ethical concerns and sustainability . . . . . . . . . . . . . . . . . . . 41.7 Societal aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.8 Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Background and theory 72.1 Striatum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Basal ganglia and related areas . . . . . . . . . . . . . . . . . 72.1.2 Network structure of the striatum . . . . . . . . . . . . . . . 102.1.3 Medium spiny neurons . . . . . . . . . . . . . . . . . . . . . . 112.1.4 Interneurons . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.1.5 Striosomes and matrisomes . . . . . . . . . . . . . . . . . . . 15

2.2 Single-cell RNA sequencing . . . . . . . . . . . . . . . . . . . . . . . 152.3 Clustering algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.1 BackSPIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3.2 Geneteams . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3.3 t-distributed stochastic neighbour embedding . . . . . . . . . 172.3.4 Relative expression index . . . . . . . . . . . . . . . . . . . . 18

2.4 Neural modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.4.1 The Izhikevich and the Multi-timescale adaptive threshold

model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.4.2 State-dependent Stochastic Bursting Neuron . . . . . . . . . 192.4.3 Iaf-cond-alpha . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.5 Network activity modes . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.5.1 What are network activity modes? . . . . . . . . . . . . . . . 222.5.2 Spike-Train Communities . . . . . . . . . . . . . . . . . . . . 22

3 Experiments 253.1 Clustering single-cell RNA sequencing data . . . . . . . . . . . . . . 25

3.1.1 BackSPIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.1.2 Geneteams . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.1.3 t-distributed stochastic neighbour embedding . . . . . . . . . 26

3.2 Neural network simulation . . . . . . . . . . . . . . . . . . . . . . . . 263.2.1 Multi-adaptive threshold neuron model . . . . . . . . . . . . 263.2.2 Spike train clustering . . . . . . . . . . . . . . . . . . . . . . 263.2.3 State-dependent Stochastic Bursting Neuron . . . . . . . . . 273.2.4 The network . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.2.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2.6 Neuron model behaviour characteristics . . . . . . . . . . . . 28

4 Results and Analysis 314.1 Striatal subtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1.1 Medium spiny neuron . . . . . . . . . . . . . . . . . . . . . . 314.1.2 Interneurons . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.1.3 Striosome and matrisome . . . . . . . . . . . . . . . . . . . . 414.1.4 Geneteams results . . . . . . . . . . . . . . . . . . . . . . . . 424.1.5 Geneuron 2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.2 Neural network simulations . . . . . . . . . . . . . . . . . . . . . . . 434.2.1 Humphries spike train communities . . . . . . . . . . . . . . . 434.2.2 t-distributed stochastic neighbour embedding clustering . . . 45

5 Conclusions 495.1 Striatal subtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.2 Neural network simulations . . . . . . . . . . . . . . . . . . . . . . . 49

6 Future work 516.1 Clustering single-cell RNA sequencing data . . . . . . . . . . . . . . 51

6.1.1 Agglomerative clustering . . . . . . . . . . . . . . . . . . . . . 516.1.2 Geneteams improvements . . . . . . . . . . . . . . . . . . . . 516.1.3 Cell Expression format . . . . . . . . . . . . . . . . . . . . . . 526.1.4 Negative Binomial Stochastic Neighbour Embedding . . . . . 52

6.2 Network activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536.2.1 Auditory encoding ISI:s . . . . . . . . . . . . . . . . . . . . . 536.2.2 Community detection algorithm and tSNE comparison . . . . 53

Bibliography 55

Chapter 1

Introduction

The heterogeneity in the brain is vast, especially when looking at mRNA expres-sion levels. We are only beginning to discover the hidden subclasses of neuronsand their functions. Researchers have recently clustered neurons into classes andsubclasses using single-cell RNA sequencing (scRNA-seq) expression levels from themouse hippocampal CA1 region and somatosensory cortex [50]. ScRNA-seq wasnamed method of the year in 2013 by Nature methods [1]. An interesting part forcomputational modellers is whether or not these subclasses have a function thatwill affect the signalling behaviour for the individual neuron or even at a networklevel. By knocking out a single gene, doing immunohistochemistry or blocking itscorresponding protein one can begin to draw conclusions about this. TypicallyscRNA-seq data has meta-data such as the diameter of the cell. This meta-datais interesting to look at when deciding upon classes but also when constructing aneural network model based on these classes. A class usually has a marker or canbe marked by overlapping markers. This enables experimentalists to look at theelectrophysiology and morphology of the class which are also crucial informationfor classification and neural modelling.

This project consists of two tasks with the first one involving classification ofsubtypes in a brain area called striatum using scRNA-seq data. The second taskasks whether or not heterogeneity in individual neurons matter on a network level,by analysing spike trains from a neural network. The first task is part of a largerproject at Karolinska Institute and will likely be published during 2016, containingexperimental confirmations. This master project presents some potentially newsubtypes of neurons the striatum.

1.1 The QUESTION

This project aims to answer several questions listed in the next sections. If one wereto compress this project into one question it would be: What neuronal subtypesexist in the striatum, and what various modes of network activity do they enablefrom a computational perspective?

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CHAPTER 1. INTRODUCTION

1.2 Task 1. Classification of neuronal types in the striatumUsing clustering algorithms such as affinity propagation and hierarchical clusteringto classify scRNA-seq data has been common and recently, new algorithms havestarted to emerge. BackSPIN is one of them and has been shown to have quite im-pressive biological relevance and robustness [50]. There is still room for improvementthough and Kenneth Harris (working with Hjerling-Leffler J.) has been developingan algorithm called Geneteams [13]. This project aims to compare, analyse and ex-plore the data from the two algorithms and see if clusters for striosome/matrisomemarkers or a third medium spiny neuron (MSN) type can be justified. The ideabehind the potential existence of a third MSN subtype came from preliminary clus-tering and expression analysis of the scRNA-seq data used in this project. ThescRNA-seq data used in this project is sampled from the mouse striatum with 1412cells in total.

1.2.1 QuestionsThe following questions were posed for task 1:

• Are there clusters which divide the striosome/matrisome markers of the stria-tum?

• How do the striosome/matrisome markers correlate with other clusters?

• What markers are there for each of the clusters?

• Is there a third MSN subtype?

1.2.2 HypothesisThe following is a list of hypotheses for task 1:

• The striosome/matrisome division can be justified with clustered scRNA-seqdata.

• There is a third MSN population in the striatum justified by scRNA-seq data.

• There are markers in the striatum which can tell us about the function of aparticular neuron class.

1.3 Task 2. Effect of neuronal heterogeneity on theactivity dynamics of the striatum

The fact that there are different subtypes of neurons with different types of spik-ing behavior and connections is known. Recent experiments show that differentinhibitory interneurons display cell type specific activity [30] and may perform dif-ferent computations [22, 45]. Despite these observations, it is not clear how this

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1.4. OBJECTIVE

heterogeneity affects the dynamics of a neuronal network. For instance, if a net-work is composed of 10 different neuron types, should it express 10 different activitymodes and each type would perform a different computational role? Previous mod-elling works suggest that the impact of a specific neuron type on the network activityitself depends on the dynamical state of the activity [36].

1.3.1 Hypothesis

The neuronal clusters observed in striatal scRNA-seq data have different functionswhich influence network behaviour and can be demonstrated by clustering spikingdata from neural network simulations.

1.4 ObjectiveThe overall objective of this project consists of the above described Task 1 and 2.More specifically the objectives of task 1 were the following:

• Cluster striatal scRNA-seq data using the BackSPIN or Geneteams algorithmswith subtypes as result.

• Create a neural network based on the subtypes from the clustering algorithms.

• cluster the spiking data from the neural network simulations and discuss itsheterogeneity.

1.5 SignificanceThis work is important to neural network modellers since it assesses the never endingmodelling thought: "Is my model detailed enough or is it too abstract?". The resultsfrom experimentalists often show great heterogeneity, but when modelling, the het-erogeneity is very often assumed to be insignificant or included in some other factor.One example is the different currents in a neuron. A very common method is toadjust the model parameters to mimic electro-physiological data, without modellingall the existing currents. Assuming that for example a certain current is not impor-tant and therefore integrated into another more general current. We will not knowif the neuron misses some functionality not captured by the electro-physiologicaldata. The same goes for networks of neurons. Let us say there is high biologicalheterogeneity in a certain neuron population. If we create a neural network thatbehaves quantitatively similar to the other network but with one or a few differ-ent neuron types (less heterogeneity), can we say they are equivalent? Or did weloose some function not captured by the quantitative measurements? If evidenceis acquired that the latter is true then computational modellers will have reasonsto build more heterogeneous models and have greater respect for the consequences

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CHAPTER 1. INTRODUCTION

of heterogeneity in neuronal populations. Experimentalists and bioinformaticianswill get clues to whether or not the differences in scRNA expression, captured bycluster algorithms, actually have an effect on the neural network level or not.

1.6 Ethical concerns and sustainability

How many lives have been sacrificed for this project? The answer depends on whatyou consider a life with value. Many mice have been sacrificed for the scRNA-seqdata used in this project. How do we justify this? Before asking this we need toanswer how it is possible that we still eat animals everyday. In a utopia I wouldlike to believe that all lives are valued equally. We value biological experimentaldata from animals high but not the consciousness that is created from it. Perhapswe value ourselves so much that the value of animals are reduced below a certainthreshold that enables experiments. Indeed this kind of data enables research thatwill be important not only to understand how the brain functions, but also diseasesand how to treat patients. With regards to why we eat animals, it has nothing todo with diseases and saving lives unless the people eating are very poor. Long be-fore preventing animal research, one should prevent eating of animals in industrialcountries. Until then, the show must go on.

This project also contains neural modelling and simulations which is a step awayfrom doing animal experiments. In the future, when our empathy regarding eatinganimals and animal experiments is developed enough, I think modelling will becomeeven more important and widely used.

1.7 Societal aspects

Why should the society care about this research? The society consists of individ-uals, with a consciousness, created by the brain, and understanding this involvesunderstanding our existence and what we are, where we came from and how weevolved. The society also has a large group of people that do not agree that con-sciousness is created by the brain. To know what types of neurons exist in the brainand how and why neural heterogeneity matters is important for the society even ifthey do not believe this though. It becomes important to most people when theyrealize this type of research is fundamental to understanding diseases and creatingtreatments for patients with brain related diseases.

1.8 Interaction

Discussion about neurobiology, molecular neuroscience and clustering algorithmswas held with Jens Hjerling-Leffler and some people in his group Carolina Bengts-son Gonzales, Hermany Mungubas, Ana B. Munoz-Manchado and his colleagues

4

1.8. INTERACTION

Kenneth Harris, Sten Linnarsson, Amit Zesiel and Hannah Hochgerner. Compu-tational neuroscience discussions regarding neural network models and simulationsand dynamical systems analysis was held with Arvind Kumar.

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Chapter 2

Background and theory

2.1 StriatumThe striatum is the primary input to the basal ganglia (BG) and receives excita-tory signals from the cerebral cortex and the thalamus [23]. The BG are a group ofsubcortical nuclei that are important for action selection and are involved in motorlearning, planning, execution, decision making, and reward related behaviour. Inorder to perform all of these tasks with the same precision as for example miceor humans, the brain needs to consider all sensory information and motor alterna-tives and select the appropriate one. The striatum is considered to be a hub forintegrating all this information into the rest of the BG [32].

2.1.1 Basal ganglia and related areas

The basal ganglia are a group of subcortical nuclei that include the striatum, sub-stantia nigra pars compacta (SNc) and reticulata (SNr), globus pallidus external(GPe) and internal (GPi) segment, and subthalamic nucleus (STN). The connectionbetween these nuclei can be seen in figure 2.1. Figure 2.2 is a more anatomicallyrealistic image of the striatum in the mouse brain, but with simplified connectivity.One often speaks of the indirect and direct pathway of the basal ganglia. From thestriatum to the thalamus one can take two paths not including the one through SNr(SNr is often ignored in this pathway). The direct pathway goes through GPi andthe indirect pathway goes through GPe, STN and then GPi [31]. For understandingthe neuronal subtypes striatum this pathway is important to have in mind.

Cortex

The striatum receives massive amounts of input from the cortex, which is why itis important to understand how the cortex projects to the striatum through thecortico-striatal pathway [49]. The main part of the cortex is the neocortex whichis divided into six (I-VI) layers of neurons of different type and connectivity [18].Layers II-VI all project to the striatum, but Layer V has the most dense connections

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CHAPTER 2. BACKGROUND AND THEORY

Figure 2.1. Connectivity within BG and some external connections. The partsmarked with green/blue are considered as part of BG. Glutamatergic connections areindicated with "+" and GABAergic with "-". Dopaminergic connections are indicatedwith "D1,+" meaning excitatory and "D2,-" meaning inhibitory.

[33]. The striatum and cortex are similar in the way that they have a small butheterogeneous population of interneurons [34] projecting onto a larger group ofprincipal cells. The interneurons have similarities but are different [39].

Thalamus

The thalamus is located between the cortex and the midbrain. It functions as arelay for sensory and motor signals but is also involved in consciousness, sleep andalertness [38]. It is sending glutamatergic axons to the striatum but also receivesoutput from the BG through GPi and SNr [31].

Pedunculopontine nucleus

The pedunculopontine nucleus (PPN) is located in the brainstem and connects tothe BG network, which is rarely mentioned in BG literature. It is divided into two

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2.1. STRIATUM

Figure 2.2. A more anatomically realistic illustration of striatum in the mouse brainwith simplified connectivity. Image taken from reference: [21].

areas, one containing cholinergic neurons and one containing glutamatergic neurons.This area is highly involved in the reticular activation system. It was recently foundto be involved in the cholinergic synaptic transmission in the striatum. Hence, acomponent connected to the striatum is connected to the system which is responsiblefor wakefulness and alertness [11, 46, 39], which could be good to have in mind whenconsidering the overall function of the striatum. The striatum is indeed bombardedwith inputs from all over the brain [32].

Amygdala

The amygdala is situated within the temporal lobe and is commonly associated withfear related reactions. That is because it has a main role in emotional reactionsbut it is also a major component in memory and decision-making [2]. It sendsglutamatergic axons to the striatum [47].

Hippocampus

The hippocampus lies in the temporal lobe underneath the cortex and is a majorcomponent in short and long-term memory and spatial navigation [51]. It has

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CHAPTER 2. BACKGROUND AND THEORY

glutamatergic axons targeting the striatum [47].

Substantia nigra pars compacta

The substantia nigra pars compacta (SNc) is heavily involved in motor activitymodulation. This was observed from animals with lesions in SNc [7]. SNc is believedto be involved in producing learned responses from stimuli. The firing frequency ofthe dopaminergic neurons in SNc is low (0.5-7.0 Hz) [24].

Globus pallidus

The globus pallidus (GP) is often divided into an internal (GPi) and external (GPe)part. GPi and GPe are both tonically active but GPe inhibit GPi and Subthalamicnucleus, and GPi continuously inhibits thalamus. If GPi were to become inhib-ited transiently by the striatum then the corresponding transient activity would betransferred to the thalamus [31].

Basal ganglia diseases and conditions

The prefrontal cortex (PFC) and the striatum are strongly related to Parkinson’s(PD) and Huntington’s disease (HD), but also non-movement related conditionssuch as schizophrenia and autism [3, 4]. In PD, degeneration occur in the dopamin-ergic connections from the SNc to the striatum. That is why many PD patientsare treated with L-dopa. HD occurs because of degenerated connections in theprojections from the striatum to GPe [31].

2.1.2 Network structure of the striatum

The striatum consist 90-95% of MSNs (depending on the species) which are GABAer-gic and thus act as inhibitory neurons. The rest are interneurons of different classesand can be GABAergic or cholinergic. The internal connectivity of the striatumcan be seen in figure 2.3. Observe that the cholinergic interneuron (Chat) to MSNconnetions are co-operating through dopaminergic synapses from SNc. Figure 2.3shows the internal connections of the striatum based on the following references:[35, 6, 15, 39, 47]. Note that this project is not covering glial cells, epithelial cellsor other non neuronal cell types that can be found in the scRNA-seq dataset usedin this project.

Figure 2.4 aims to show the heterogeneity of celltypes in striatum but also thatmany celltypes although different have similar firing properties such as low-thresholdspiking (LTS) and fast spiking interneurons (FSIs). Observe that there are somemore Htr3a co-expressing populations (for example Th) as shown in previous studies[27].

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2.1. STRIATUM

Figure 2.3. Connectivity within the striatum and some external connections. Ovalobjects are brain structures and rectangles are neuron subtypes. Connection arrowswith no target means that it targets the striatum, but information about the exactsubtypes targeted was not known/found, inconsistent or not relevant for this project.

2.1.3 Medium spiny neurons

There are two types of MSNs marked by Drd1a (MSN1) and Drd2 (MSN2), that aregenerally accepted [39]. The division of these subtypes is strong because of the clearmarkers (see table 2.1 but also because of the difference in functionality. MSN1 isinvolved in the direct pathway whereas MSN2 is involved in the indirect pathway.Some support exists for a third population of MSNs which contain both D1 and D2receptors (MSN-D1/D2) [29, 47, 10]. It is an ongoing debate and one of the thingsthat will be discussed in this report. Table 2.1 shows some of the markers for MSNand its subtypes. Commonly used MSN markers are Bcl11b or Gpr88 [27]. Thefiring frequency of striatal medium spiny neurons lies between 0.2-20 Hz [49].

2.1.4 Interneurons

The heterogeneity of interneurons in the striatum is vast even though they make uponly 5% of the striatal neurons [42]. Lhx6 and Ncald are two interneuron markers[41]. In the following subsections more or less hypothetical interneuron subtypes willbe presented. These subtypes will serve as a basis for analysing the subtypes foundin the results and analysis section. Table 2.2 shows a list of interneuron subtypemarkers. Researchers have been asking whether or not Vip and Cck expressing

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CHAPTER 2. BACKGROUND AND THEORY

Figure 2.4. Different cell types in the striatum including hypothetical types. Thefirst column of arrows indicate hierarchy. The second column of arrows indicaterelations to functionality based subtypes or other reported subtypes.

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2.1. STRIATUM

Subtype MarkerMSN Bcl11b, Gpr88MSN1 Drd1aMSN2 Drd2, Adora2a

Table 2.1. Subtype markers for MSNs.

striatal cells are marking two subtypes, so far without obtaining any conclusiveevidence for either case [42].

Subtype MarkerInterneuron Lhx6Parvalbumin PvalbNpy Sst, Npy, Nos1, ChodlCalretinin Calb2Cholinergic ChatSerotonine Htr3aCholecystokine Cck?Vasoactive intestinal polypeptide Vip?

Table 2.2. Subtype markers for striatal interneurons.

Pvalb+ interneurons

Parvalbumin (Pvalb) expressing interneurons, also known as fast-spiking interneu-rons (FSIs) and sometimes called PV instead of Pvalb [20]. It can spike withfrequencies around 200 Hz and in some instances over 400 Hz. In 2010, Pvalb wasthe only striatal neuron which had been observed morphologically to have gap junc-tions. They:1) do not fire spontaneously,2) have low input resistance 50− 150MΩ,3) are similar to the FSIs reported in Hippocampus and Cortex, and4) cannot sustain repetitive firing at low frequencies.It is possible there are two subtypes of Pvalb-interneurons distingushed by onegroup that fires continuously and another group that fires in a stuttering manner.This might be two states in one subtype though. The functional role of FSIs in thestriatum is to exhibit feed-forward inhibition onto the MSNs for spike time control[42]. They also connect to other FSIs but only sparsely to LTS and not at all tocholinergic [41].

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CHAPTER 2. BACKGROUND AND THEORY

Calb2+ interneurons

Calretinin (Calb2) expressing interneurons, sometimes called CR instead of Calb2,is a small group of interneurons and in 2010 there were not even any recordingsmade yet from this subtype. 0.5% of the striatal neurons in rat are Calb2+. Thissubtype is much more common in primates than rodents. [42]

Npy+ LTS interneurons

Neuropeptide-Y (Npy), nitric oxide synthase (Nos) and somatostatin-expressing(Sst) interneurons has previously been clustered into one group, with the potentialfor more subtypes [42] as shown by reference: [27]. This group is equivalent to thelow-threshold spiking persistent depolarizing plateau potential interneurons (PLTS)[42]. A large portion of the cells exhibit tonic spontaneous activity in mice but notin rats [39].

Npy+ NGF interneurons

Neuropeptide-y expressing (Npy+) neurogliaform (NGF) is another small interneu-ron population. Information about these smaller subtypes is hard to find. NGFcells have also been found in the Htr3a expressing interneurons [16] This celltypedoes not seem to exist in rats, but they definitely do in mice [39].

Th+ interneurons

The tyrosine hydroxylase (Th) expressing interneuron has been found to have fourelectro-physiologically different subtypes. All of the type IV has a LTS compo-nent. None of the types are similar to MSNs, SNc dopaminergic neurons, FSIs orcholinergic interneurons [15].

Htr3a+ interneurons

This is a large group co-expressing with Pvalb around 20%. Two distinct subtypesnot overlapping with Pvalb or Npy/Sst/Nos1 have been identified. A late-spiking(LS) Htr3a-NGF population, that is Npy-negative, and a larger group with LTS-likeactivity even though they are Npy/Sst/Nos1-negative. The Htr3a-LTS intenreuronsdiffer from other LTS in that they respond to nicotine administration [27]. It wouldbe interesting to analyse the differences between the NPY-NGF/LTS and the Htr3a-NGF/LTS populations.

Chat+ interneurons

Cholinergic interneurons (Chat) are not similar to the other interneurons in stria-tum. Intead of GABAergic synapses it forms cholinergic synapses with MSNs andother interneurons. Axons from neurons SNc connect to this interneuron but axons

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2.2. SINGLE-CELL RNA SEQUENCING

Figure 2.5. Example of scRNA-seq pipline. Image taken from reference: [50].

from GABAergic interneurons in striatum has also been reported. They make up0.5-1% of the striatal interneurons. They are tonically active [39].

Vip/Cck+ interneurons

Vasoactive intestinal polypeptide (Vip) and Cholecystokine (Cck) are two potentialsubtypes that are hard to find and low in numbers [42]. This also applies to findinginformation about these subtypes in literature.

2.1.5 Striosomes and matrisomes

MSNs can as mentioned above be divided into the two groups with one contributingto the indirect pathway (Drd2) and the other to the direct pathway (Drd1a). Butthey can also be divided into striosome and matrisomes also called patch and matrix.The idea is that striosomes and matrisomes have different functions in the striatumbut it is not entirely understood how. The matrix is part of the sensory-motorand associative circuit, and the striosome receives input from the limbic cortex andproject to SNc. More specifically it has been suggested that striosomes enable usageof limbic information on sensory-motor and associative behaviour [8].

2.2 Single-cell RNA sequencing

ScRNA-seq is a method that can capture the transcriptional state of a single cell.Recall that in a cell, DNA is transcribed to RNA, and RNA tells the cell whichproteins to synthesize, but is also involved in catalysis of biological reactions, geneexpression control, and cell communication [37].

Figure 2.5 shows the overall process of gathering and working with scRNA-seqdata. A more detailed description of the method used for obtaining the scRNA-seqdataset used in this project can be found in the upcoming publication of this data,likely during 2016.

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CHAPTER 2. BACKGROUND AND THEORY

2.3 Clustering algorithmsGene markers have often been the way that one defines a group of cells. This is notoptimal since there are more than 24000 genes that need to be considered. Alsomarking a cell group is just one way of defining a group. Another way could be thattwo genes co-express a certain group of cells or that every cell in a group, expressedby gene1, also express gene2 except for a certain subset. These kinds of relationshipsare hard to find manually, but not impossible for a computer clustering algorithm.

2.3.1 BackSPINBackSPIN [50] is an unsupervised divisive clustering algorithm based on the SPINalgorithm [43]. SPIN stands for Sorting Points Into Neighbourhoods and the maindifference between SPIN and BackSPIN is that SPIN does not identify clusters,which is the whole point with BackSPIN. SPIN is used to sort the distance/correlationmatrix and results in a specific order for the features. BackSPIN can be describedwith three steps.1. Sorting the cells and genes in the expression matrix using the SPIN algorithmon the unsorted distance matrix resulting in a sorted distance matrix R.2. Splitting the expression matrix using R in the following function,

S(i) =∑i

j,h=1Rj,h +∑N

j,h=i+1Rj,h

i2 + (N − i)2 , (2.1)

for all i = (1, ..., N), creating a vector of Scores S. Isplit = index of max(S(i)). Nowwe have the splitting point Isplit and want to see if it is relevant to make a split.We calculate Sleft and Sright in which we essentially calculate a ratio between themean of the sub-matrix of the correlation and divide it of the mean correlation inthe whole matrix. This ratio will tell us if the split is good or not.

Sleft =∑Isplit

j,h=1Rj,h

I2split

/∑Nj,h=1Rj,h

N2 , (2.2)

Sright =∑N

j,h=1+IsplitRj,h

(N − Isplit)2

/∑Nj,h=1Rj,h

N2 , (2.3)

Only split if max(Sleft, Sright) > 1.15. However this cut off value can be adjustedas preferred. Also genes are assigned to groups in this step depending on where itis expressed the most.3. Recursion on the each sub-matrix consisting of the cells and genes assigned toeach half.

2.3.2 GeneteamsGeneteams is a semi-automatic algorithm with a new way of selecting candidatefeatures as input to divisive hierarchical clustering. "Team scores" are defined as

16

2.3. CLUSTERING ALGORITHMS

Xc = xc ·w1 and Yc = xc ·w2 where xc is the expression of one cell and w anon-zero vector that tells each cell to what degree it belongs to either team 1 (w1)or 2 (w2). To measure how good well the teams are at dividing the cells into twogroups the following function is used:

f(X,Y ) = (X + 0.5)2 + (Y + 0.5)2

(1 +X + Y )2 . (2.4)

To account for the fact that this function can be maximized by either increasingw or maximization of number of cells in one class, it is complemented by twopenalty terms that can be found in the original article [13]. The enormous searchspace that this problem would require is managed by selecting a subset of 100genes. This is done by using the fact that scRNA-seq data is negative binomiallydistributed, and if a gene strays from this distribution, it probably does so becauseit is describing a subset of cells. Genes that are expressed only in low levels arealso sorted out. As mentioned above, the algorithm is semi-automatic in its currentform. This can be considered a strength and a weakness. The function describedabove is used recursively so that a hierarchical structure is created from the cells ina divisive manner. At before each split is made the user is required to analyse theexpression and score plots and make the decision of whether or not to split. Userswith high knowledge of biology and genes can benefit from this since they can usetheir knowledge to make better split decisions. This unfortunately also makes thealgorithm very time consuming for the user since there can be a lot of splits in largeheterogeneous datasets.

2.3.3 t-distributed stochastic neighbour embedding

The visual clustering technique t-distributed stochastic neighbour embedding (tSNE)is a machine learning algorithm designed for dimensionality reduction, essentiallyused in the same way as principal component analysis (PCA). The tSNE algorithmis very common among scRNA-seq researchers which I personally can confirm afterattending the Single-Cell Genome conference in Utrecht 2015. It is popular becauseof its ease of use and power in separating data points for scatter plotting comparedto other methods such as PCA.

To understand tSNE one must recall the normal (gaussian) distribution and thet-student distribution which are the building blocks of tSNE. Let x1, ..., xi be allthe single cells (data points) with N genes (dimensions). First tSNE computes theprobabilities pij in order to find out the similarities between all combinations ofcells xi and xj with the following functions:

pj|i = exp(−||xi − xj ||2/2σ2i )∑

k 6=j exp(−||xi − xj ||2/2σ2i ), (2.5)

pji =pj|i + pi|j

2N . (2.6)

17

CHAPTER 2. BACKGROUND AND THEORY

In equation 2.5 one may recall the gaussian equation used in a normal distribution.The nominator of the similarities pj|i is the probability that xi comes from thenormal distribution defined by xj which is used instead of the mean µ or horizontalposition of the bell curve, together with a suitable σi. The nominator is thennormalized by the sum of the same measurement but with all other data pointcombinations with i instead of j. pji adds them both together so that it becomes atwo sided measurement of similarity. So now we have the similarities and need tocreate a d-dimensional map y1, ..., yN with yiεR

d (yi could for example be a 2D pointin a scatter plot). Since we do not know what these values are in the beginning theywill be initialized by using randomly selected values from a student-t distribution.This is why the tSNE scatter plots can look different when rerunning. For measuringhow similar yi and yj are the following function is used,

qj|i = (−||yi − yj ||2/2σ2i )−1∑

y 6=j (−||yi − yj ||2/2σ2i )−1 . (2.7)

As you can see, the equation above is in a way similar to 2.5 but based on thestudent-t distribution. Now, we just have to find a way to give each of the datapoints yi and yj the correct amount of similarity (measured by 2.7) which pji can tellus about. A good way to solve this kind of problem is to use the Kullback-Leiblerdivergence,

KL(P ||Q) =∑i 6=j

pijlog(pij

qij), (2.8)

and minimize it using gradient descent [25].

2.3.4 Relative expression indexIn order to find cluster markers a relative expression index (REI) was calculatedfor each cluster. Observe that this method requires that the scRNA-seq data hasalready been clustered. Intuitively one can easily realize that you can use the meanof the cluster and divide it by the mean µ of the rest of the clusters to get genesthat are highly expressed. In reference [13], REI is defined as:

REI = µ1 − µ2(µ1 + µ2 + 1) , (2.9)

where µ1 is the mean for the cluster you are interested in and µ2 is the mean for therest of the clusters. Another possibility is to use the mean and standard deviation σto calculate a REI, since σ could tell us how stable the expression is in each cluster.Here is an example on how this can be implemented:

REI = µ1(µ2 ∗ σ2) , (2.10)

where σ2 is the standard deviation for the rest of the clusters. A more standardizedform is needed so that the scale goes between two fixed values such as 0 and 1 as in

18

2.4. NEURAL MODELLING

equation 2.9. The fraction between µ1 and µ2 tells us about the relative expressionand if σ2 is high it means there is some varying expression outside of the cluster weare interested in which is not good for a marker.

2.4 Neural modelling

2.4.1 The Izhikevich and the Multi-timescale adaptive threshold modelThe Izhikevich model is a very time efficient neuron model relative that essentiallycan demonstrate all functions that a Hodgkin Huxley model can as shown in thefigure 2.6.

The main drawback of the Izikhivich model is that it has non-linear dynamicswhich only enables approximations of numerical solultions [17].

Equation 2.11 shows the original Multi-timescale adaptive threshold (MAT)model [19]. The original MAT model could only reproduce 9 out of 20 firing modes.However the MAT with voltage dependent firing threshold can produce all 20 firingmodes shown in the Izikhivich description [48]. The MAT model is given by,

τmdV

dT= −V +RI(t), (2.11)

with a spiking threshold rule given by,

θ(t) =∑

k

H(t− tk) + w (2.12)

and,

H(t) =L∑

j=1αjexp(−t/τj), (2.13)

with variables as given in table 2.3.No previous network model of the striatum using the NEST simulator and the

MAT or Izhikevich model is available in the literature. NEST stands for NEuralSimulation Tool and is a computer program for simulating large neural networks[12]. A striatal model with integrate-and-fire (iaf) neurons was published recently[5]. Models have been made in GENESIS [44] with 1049 Hodgkin Huxley typeneurons [9]. The simulation time would most likely be to high using the amount ofneurons that this project requires. Choosing integrate and fire neurons would becomputationally possible but a lot of neuron firing modes would be lost. The MATmodel is a good middle ground alternative since it is computationally efficient yetstill has high firing complexity.

2.4.2 State-dependent Stochastic Bursting NeuronThe State-dependent Stochastic Bursting Neuron (SSBN) is a new neuron model.With this neuron it is possible to control the bursting of an integrate and fire neuron

19

CHAPTER 2. BACKGROUND AND THEORY

Figure 2.6. Firing modes that Izhikivich model is able produce. Image taken fromreference: [17].

20

2.4. NEURAL MODELLING

Variable Descriptionτm membrane time constantV(t) model potentialR membrane resistanceI(t) input currenttk kth spike timeL Number of threshold time constantsτk(j = 1, ..., L) jth time constantsαj(j = 1, ..., L) weights of the jth time constantsw resting value

Table 2.3. Variable explanation for equations 2.11, 2.12 and 2.13.

without changing the frequency-intensity curve [36]. The SSBN is originally a leakyIAF neuron and therefore it has the same base equation as the MAT model equation(2.11). It does not however have an adaptive threshold. Instead it has a constantpredefined threshold u at which a burst is fired with probability 1/b, with b spikesin the burst. There also exists a modified SSBN version in which the number ofspikes b (spikes/bursts) is set to be a function of the mean input current that aneuron receives. It is drawn from a binomial distribution b ∼ B(n, p), where n isthe maximum number of spikes per burts and set to n = 4, and p is the probabilityof one spike.

2.4.3 Iaf-cond-alphaThis is a simple integrate-and-fire neuron model (iaf-cond-alpha) with conductance-based alpha-shaped input current [26] and is defined by the following equation :

cdV

dT= −Ileak − Ispike − Isyn, (2.14)

where c is the capacitance, Ileak is the leak current, Ispike is the current for theneuron spiking mechanism, and Isyn is the synaptic input currents. The leak currentis defined by:

Ileak = cV − Vp

τp, (2.15)

where Vp is the resting potential and τp is the membrane time constant. Ispike

triggers a spike at a certain predefined threshold and is defined as:

Ispike = c

(dV

dT

)−1

V =Vth

(Vth − Vr)δ(V − Vth), (2.16)

where Vth is the firing threshold, Vr is the reset voltage and δ is the delta (or dirac)function. The explicit equation for Isyn can be found in reference: [26]. It is not in-cluded to decrease the amount of equations and variables in this report. Essentially,

21

CHAPTER 2. BACKGROUND AND THEORY

Isyn describes how the inputs from inhibitory, excitatory and external excitatoryneurons is interpreted. In iaf-cond-alpha the synaptic current is conductance-basedas opposed to iaf-neuron on which the SSBN neuron is based on. They also have adifferent framework as one can easily notice by looking at the C++ source code.

2.5 Network activity modes

2.5.1 What are network activity modes?

How can the network activity modes (NAM) be analysed? Should one analysefrom a neuron perspective as in scRNA-seq clustering, or should one cluster basedon time? NAM can be analysed from both of these perspectives but they give usdifferent answers. If one clusters from a neuron perspective, we basically ask: Canwe separate the neurons based on their individual spike trains? And if one clusterdifferent time steps we ask: If each neuron is a singer in a choir, can we divide thesong into different parts? We initially considered the neuron perspective. But theanalogy of neurons being singers led me to develop an alternative way of lookingat spiking activity. Why is it that we always look at things? Of course the eyeis probably our most powerful sense, but could we be missing something by notusing our perhaps second most powerful sense, the hearing? In principle I wantedto convert two spike trains from two connected neurons and see what they soundlike. Direct conversion of spike train to sound has of course been done but I wantedto convert spike trains into sinus waves. By taking the distance between two spikes,also called the inter spike interval (ISI), and using that distance to insert a sinewave scaled with the ISI, we can encode a whole spike train and make each neuronsing a tone varying with the ISI. Since each spike train is scaled equally we can hearharmonies between the tones no matter how the tones are scaled. Scaling is doneboth to slow the tones down and to bring them to a suitable (human) hearing level.This could be viewed as art but it also has some scientifically interesting factors.Are the neurons forming some kind of activity mode or synchrony when the neuronsform a chord?

2.5.2 Spike-Train Communities

The term "Network activity modes" is used to imply that it is the activity from theneuron perspective that is to be analysed. A different term is preferable since onecan interpret it as activity from a time perspective (instead of neuron). Mark D.Humphries has done analysis from both time and neuron perspective. The analysisfrom the neuron perspective is called "Spike-Train communities", which I find suit-able [14]. He has created an algorithm capable of finding groups of similar spiketrains. One of the unique aspects is that it also determines the number of groupssuitable for a certain set of spike trains.

22

2.5. NETWORK ACTIVITY MODES

When clustering sets of spike trains a question that comes up is whether ornot to use binning of time intervals so that its possible to sum all spikes in eachinterval and use that value instead of 1 and 0 (spike or no spike). There will ofcourse always be some kind of time interval, but the question is whether or not toallow multiple spikes or not in each interval. Humphries algorithm allows for bothand the binned version converts each sum to 1 if at least one spike occurred in theinterval. The binned version calculates the proportion of bins that differed betweeneach pair of vectors (using hamming distance) and puts it in a comparison matrix C.

The binless version uses a Gaussian kernel to create a continuous vector thatrepresents the spike train. To compare this type of vector he uses the cosine anglebetween each pair combination of spike trains to create the comparison matrix C.This comparison matrix is visualized by using a network where each node is a spiketrain and each connection between two nodes are the similarity between the spiketrain with thickness encoded to the connection line.

By taking the difference between the number of connecting nodes for each pairof nodes and the number of expected nodes defined by a null model one gets thematrix B. The number of positive eigenvectors of this matrix is then used to splitthe spike trains into groups. The main difference between Humphries and previousalgorithms is that he makes use of all the eigenvectors instead of only using theleading one, he uses the full comparison matrix instead of applying a filter functionwhich saves information, and instead of splitting in two iteratively, the maximumnumber of groups is calculated from the number of positive eigenvalues of B. B isdefined as:

B = C − P, (2.17)

where C is the comparison matrix and P is a null model created from the links inC. The maximum number of groups is defined as M = n+ 1 where n is the numberof positive eigenvalues in B. To manage group membership the matrix S is used,and defined as:

Sij =

1, if node i is in group j0 otherwise

The goal of the algorithm is to maximize the following equation:

Q = Tr(StBS), (2.18)

where Tr (trace function) calculates the sum of all diagonal elements, and t meanstranspose. The actual splitting is done using the k-means algorithm which needs apredefined number of clusters K. In this case K is the number between 2 and Mthat maximizes Q.

23

Chapter 3

Experiments

3.1 Clustering single-cell RNA sequencing data

A recently compiled scRNA-seq dataset from the striatum containing 1412 sampleswas used to create the clusters using the the BackSPIN and Geneteams algorithm.The cells were taken from mice at age 20-28 weeks.

A program called Geneuron 2.0 with a graphical user interface was developedto simplify repetitive plotting and analysis and enabling users with low program-ming knowledge to participate in the analysis. It is a great enrichment for theanalysis process to include people with excellent biological knowledge. A similarweb-program called Geneuron 1.1 was developed in earlier work for simplifyinganalysis of co-expression in multiple genes [50].

In later stages of clustering after main groups already had been found, activitydependent genes were removed. A gene was considered activity dependent if theexpression increased more than 3 fold during 1 or 6 hours. After this was donemany of the smaller groups defined by a marker were less clear. The list of activitydependent genes were taken from: [40].

3.1.1 BackSPIN

BackSPIN clustered the data hierarchically by splitting each group once per level.Once the main groups (MSNs/interneuron/others) had been decided the groupswere split again, this time without hierarchy but a specific number of predefinedsplits. All backspin clustering was performed by Amit Zesiel, from Sten Linnarssonslab at KI, since he had an improved version of the algorithm. The difference fromthe currently publicly available version (the one described in previous chapter) isnot significant though. My task included analysing the resulting clustering, findinggene markers for each cluster, integrating the new clusters in Geneuron 2.0 andcomparing with Geneteams and tSNE. Analysis of the clustering included lookingat different cell labels such as diameter or strain to see if there were any differencesbetween clusters or if a certain cluster contained more of a certain strain than one

25

CHAPTER 3. EXPERIMENTS

could expect by random chance. I will only show the most relevant analyses andresults so as not to overburden the reader.

3.1.2 GeneteamsGeneteams clustering was performed in parallel to give greater legitimacy to theclusters The algorithm is semi-automatic and was executed with the following guid-lines: Keep splitting while:1) you get a "L-shaped" plot of the team scores,2) division is not based on sex-specific genes (Xist, Tsix etc),3) the number of genes in the teams is reasonably small (less than 20),4) and the cells are not over fitted, which can happen if there are few cells.

Using all 1412 cells, it could take up to a week to perform a complete clustering.This of course depends on the user and how detailed clustering one desires.

3.1.3 t-distributed stochastic neighbour embeddingClustering using t-distributed stochastic neighbour embedding (tSNE) was per-formed but not mainly to gain legitimacy to the clusters but to visualize the clus-tering in different ways. To cluster with tSNE using all genes cells at once is notoptimal. Contamination, activity dependent genes, and quality differences affectsthe clustering a lot.

3.2 Neural network simulation

3.2.1 Multi-adaptive threshold neuron modelThe multi-adaptive threshold (MAT) neuron model was tested but not used inthe end because the NEST version of the MAT model does not generate the samebehaviour as in the original papers when using type specific parameters. HansPlessner could provide the parameters one should use to reproduce the behaviourof the original articles. I also tested a MAT model adapted by a student at theUniversity of Freiburg, Germany, to reproduce spiking behaviour of MSNs. Themodel was not yet adapted to the NEST framework so more time would be neededin order to test it for the purpose of this project. Both of these MAT models seempromising in near future.

3.2.2 Spike train clusteringHumphries algorithm [14] was used on the simulation data. I also created a clus-tering approach consisting of the follow steps:-Creating a more suitable data vector for each spike train by binning on low dimen-sion (1ms),-Scaling up the numbers by multiplying by 10000,

26

3.2. NEURAL NETWORK SIMULATION

Figure 3.1. Illustration of the neural network model that was used. Arrows indicateglutametergic connections, and the cirle indicates GABAergic connections.

-Using a gaussian kernel with a certain standard deviation to smooth the curve,-Insert the vector to tSNE for clustering using two dimensions.

This results in a 2D plot representation of each spike train. The neurons werelabelled so that one could see the type and whether or not it clustered separately.

3.2.3 State-dependent Stochastic Bursting Neuron

The State-dependent Stochastic Bursting Neuron (SSBN) was integrated to a net-work based on striatum [5] . Since the network was built with IAF neurons it is verygeneral even though I apply it to the striatum. The network was built in NESTusing the iaf-cond-alpha neuron model together with SSBN.

3.2.4 The network

I used a model developed by Bahuguna et al. [5] as a basis to create a model ofthe striatum. The network included four types of neurons: MSN-D1, MSN-D2,FSI and SSBN, where the SSBN is the new component compared to the network inBahuguna et al. All membrane potentials where initiated at a random level between-80 and -60. Input from cortex and poisson noise to each neuron was set to 5000Hz. Figure 3.1 is an illustration of the neural network models that was used. Theconnection probabilities for the network are stated in table 3.1 and the propertiesfor the neurons in the network is stated in table 3.2. For more parameters you maylook in the code shared with this project.

27

CHAPTER 3. EXPERIMENTS

MSN-d1 MSN-d2MSN-d1 0.13 0.03MSN-d2 0.135 0.18FSI 0.27 0.18SSBN 0.27 0.18

Table 3.1. The following connection probabilities were used for the the networksimulation. Connection direction is indicated by row->column.

Subtype: MSN d1/d2 FSI SSBNThreshold (mV): -45 -54 -59Resetting level (mV): -80 -80 -65Resting membrane potential (mV): -80 80 -70Capacitance of membrane (pF): 200 500 150

Table 3.2. The following properties where used for the neuron models in the simu-lation.

3.2.5 SimulationsThere were two main simulation setups that were constructed to show a differencein clustering by altering the heterogeneity. Setup 1 had a low amount of SSBNsand Setup 2 had a relatively high amount of SSBN as shown in table 3.3.

Subtype Setup 1 Setup 2MSN-D1: 500 500MSN-D2: 500 500FSI 80 80SSBN 20 100

Table 3.3. Simulation setups.

3.2.6 Neuron model behaviour characteristicsThe characteristic behaviour defined by a frequency intensity (FI) curve and transferfunction (TF) for each of the neuron types can be seen in figure 3.2. D1 and D2had the same parameters. The reason they differ in behaviour in the network isbecause of the different synaptic connection probabilities.

28

3.2. NEURAL NETWORK SIMULATION

(a) FSI (b) FSI

(c) D1/D2 (d) D1/D2

(e) SSBN (f) SSBN

Figure 3.2. Transfer function (y=inhibitory, x=excitatory, poisson frequency input),and frequency intensity curve for each of the neuron types. This input represents totalexcitatory and inhibitory input impinging on a MSN.

29

Chapter 4

Results and Analysis

4.1 Striatal subtypes

Early in this project it was established that there was a clear main hierarchy ofneuronal cells consisting of MSNs, interneurons, and another large group with non-neuronal cells which this project does not focus on. These main types can be seenin figure 4.1. The non-neuronal cells called "others" in figure 4.1 are clearly sep-arated from the neuronal and consist of for example astrocytes, oligodendrocytes,microglia, cycling cells, endothelial cells, ependymal cells. For analysis of this stri-atal dataset from a non-neuronal cell perspective you may look for publicationsfrom Goncalo lab at the Karolinska Institute. Figure 4.2 shows the expression of all1412 cells. At the far right in the figure one can see some cells that were initiallylabelled as the neuronal subtypes but were removed later because of low quality(low molecule counts).

4.1.1 Medium spiny neuron

There is a clear division of the medium spiny neurons (MSNs) into two groups MSN1and MSN2. Apart from this clear division there are two additional possible groupsMSN3 and MSN4. MSN3 is marked by MSN3-gene1, and can be seen in figure4.4. They are a bit smaller cells and have slightly lower quality compared to theother MSNs. MSN4 is is defined by the co-expression of MSN1 markers and MSN2markers. There are 696 cells expressing either Drd1a or Penk, and 122 cells expressboth. That said MSN4 is was not a clear defined cluster in any of the clusteringalgorithms BS, GT or tSNE. Therefore MSN4 is not included as a separate group.These markers can be seen in figure 4.3.

Considering the BS, and tSNE clustering and expression of marker genes I con-clude that there are most likely 3 MSN subtypes, as shown in figure 4.5.

31

CHAPTER 4. RESULTS AND ANALYSIS

Figure 4.1. TSNE plot of all 1412 cells labelled with general types interneurons,MSNs , other.

Figure 4.2. Expression plot of all 1412 neurons labeled with general types interneu-rons, MSNs , other.

32

4.1. STRIATAL SUBTYPES

Figure 4.3. Expression plot of MSN subtype marker genes.

33

CHAPTER 4. RESULTS AND ANALYSIS

Figure 4.4. tSNE plot with MSN marker Gpr88, Drd1a, Penk and MSN3-gene1.

4.1.2 Interneurons

In BackSPIN interneurons are first of all separated from MSNs and other non-neuronal cell types which can be seen by marker Ncald in figure 4.6. At this levelof the hierarchy all interneurons (including Chat cells) are included. After this, theSst expressing cells are clustered separately and the rest of the interneurons can bedescribed by a large Kit/Htr3a group and the Chat cells.

Chat+ interneurons

Chat cells seem to be one fairly strong cluster. However note that when visualizedwith tSNE, some Chat cells formed a small subgroup. Also Ntrk1 marks a subsetof the Chat cells.

34

4.1. STRIATAL SUBTYPES

Figure 4.5. Hierarchical illustration of MSN subtypes.

Pthlh+ interneurons

A larger group was found in both BS and GT marked by Pthlh and Cox6a2. Thisgroup contains subgroups Pvalb and Pthlh (Pvalb-). There were indications of moresubtypes but no clear markers were found for them.

I find it interesting that Gadd45g is expressed in the NG cells and at the sametime in Pthlh. It is known that a large part of the Htrta/Kit+ cells are NG cells[27]. Could Gadd45g be a NGC marker?

Th+ interneurons

Th shows one relatively clear cluster. Gal, Cadps2 and Chrna3 are possible markersof a subtype within this big Th cluster. But the latter two are possibly just lowexpressing genes that otherwise would express the whole Th cluster. As describedearlier in this report, there is supposedly 4 types of Th neurons from a electro-physiological viewpoint. Two larger and two smaller groups. At least we can assumethat there is heterogeneity withing the Th expressing cells. The two large groupscould be explained by the markers Gal, Cadps2 and Chrna3. The smaller groupscould be explained by the Th expressing cells that are "outside" of the main Thcluster. For example there are Th+ neurons in the Sst, Pthlh (and Gadd45g) andPvalb clusters. 40/332 ([gene1 and gene2]/[gene1 or gene2]) express both Th andPthlh. 26/231 cells express both Th and Pvalb. Although not many in the main

35

CHAPTER 4. RESULTS AND ANALYSIS

Figure 4.6. tSNE plot with interneuron markers Ncald, Chat, Npy, Kit.

Pvalb cluster. 7/274 cells express both Th and Sst. The Gal marker is a veryinteresting subtype of the Th+ cells. In a study [50] using scRNA-seq data fromcortex and hippocampus, an interneuron subtype named Int14 have this markerexpressed relatively clear as seen in figure 4.11. Gal stands for Galanin and is aneuropeptide on which plenty of research has been done. One study linked Gal inthe mouse striatum to consummatory behaviours [28].

Sst+ interneurons

Sst and Npy are as previously shown often used to mark the same group. Sst andnot Npy was chosen as the marker for these cells since there exists a small populationof Npy+ and Sst-. The expression of Sst can be seen in figure 4.8.

NGF interneurons

There are NGF cells in the Npy+ cells labeled Npy-NGC in figure 4.7. ThePthlh/Kit/Htr3a+ population has a larger population of NGF cells and ultimately

36

4.1. STRIATAL SUBTYPES

Figure 4.7. Expression plot with interneuron markers Ncald, Chat, Npy, Kit.

37

CHAPTER 4. RESULTS AND ANALYSIS

Figure 4.8. Expression plot with interneuron subtype markers Chat, Sst, Pvalb,Th.

38

4.1. STRIATAL SUBTYPES

Figure 4.9. Expression plot with interneuron subtype markers Crhbp, Gadd45g,Gal, Cadps2.

39

CHAPTER 4. RESULTS AND ANALYSIS

Figure 4.10. Hierarchical illustration of interneuron subtypes.

Figure 4.11. ScRNA-seq expression of Gal in cortex/hippocampus. Figure takenfrom linnarssonlab.org/cortex [?].

40

4.1. STRIATAL SUBTYPES

Figure 4.12. Image of matrisome genes with Striatal scRNA-seq expressionheatmap.

one would like a marker for all NGF cells. I believe Gadd45g is a candidate for this.This gene can be seen in figure 4.9. But note that this is very hypothetical andexperiments is needed to confirm this claim. All I note is that Gadd45g is epressedby the Npy-NGC and has a population in the Pthlh/Kit/Htr3a+ population. Pnocis a gene that marks this small group relatively clear.

Calb2+ interneurons

There were 14 Calb2 expressing cells 4 of which were co-epressing Npy and Sst. Onecould still call it a subtype but it is not sorted neatly into any cluster. This couldbe because Calb2 is low expressed in general with a peak of 5 molecules/cell.

Vip+ interneurons

There were few Vip+ cells and they had low expression except for one cell with 80molecules/cell. Interestingly 14/20 Vip cells co-express Th including the one with80 molecules/cell.

Cck+ interneurons

Cck has low expression with a peak of 11 molecules/cell and is spread out in manydifferent clusters. This gene does not seem to mark any subtype.

4.1.3 Striosome and matrisomeThe markers for matrisomes and striosomes are shown in figures 4.12 and 4.13 but nospecific group or pattern was detected by manual inspection of BackSPIN clustered

41

CHAPTER 4. RESULTS AND ANALYSIS

Figure 4.13. Image of striosome genes with Striatal scRNA-seq expression heatmap.

(and ordered) data. This strengthens the hypothesis that the patterns observed inthe striatum that are called striosomes and matrisomes are marked by the axons tothe striatum, originating from other structures than the striatum, and not so muchby the striatal neurons themselves.

4.1.4 Geneteams results

The results for the Geneteams algorithm was unfortunately not so robust since itis user dependent and seems to sometimes suggest gene markers that are widelyexpressed but has local variation. Therefore, I decided that the main clusteringwould not be made using this algorithm. Instead I used it to confirm subtypes. Soif a subtype with a certain marker was found both in BackSPIN and Geneteams,it would be considered a stronger type rather than if it was only found in one.However none of the subtypes were determined solely by Geneteams.

4.1.5 Geneuron 2.0

During this project repetitive plotting and analysis of different genes were per-formed. A lot of time can often be spent on generating figures and comparingdifferent genes with each other using different labels such as clustering, chip-id or,strain etc. Geneuron is an application with a user-friendly interface adapted sothat even people without programming skills should be able to generate plots andperform data analysis. The idea of Geneuron started while working on a similarscRNA-seq dataset of cortex and hippocampus from the same labs [50], and is hostedon hjerling-leffler-lab.org. Geneuron 2.0 is quite different and more user friendly,

42

4.2. NEURAL NETWORK SIMULATIONS

Figure 4.14. Spiking activity in a network model of the striatum with four types ofneurons.

but is built on the same idea, programming language (R) and packages (Shiny).Geneuron 2.0 will be shared when the dataset is published.

4.2 Neural network simulations

The spiking behaviour of the neurons during the simulation can be seen in figure4.14. In this simulation one can even intuitively see that there is heterogeneitybetween and within the neuron groups.

4.2.1 Humphries spike train communities

As seen in figure 4.15 and 4.16, low heterogeneity is demonstrated using the simu-lation data. There were 2 groups when the network simulation only had 20 SSBNs(setup 1), as seen in figure 4.15. There were 3 groups when the network simulationhad 100 SSBNs (setup 2), as seen in figure 4.16. The increase in heterogeneity thatone would expect by including more SSBNs is shown by the increase of groups from2 to 3 between setup 1 and 2.

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CHAPTER 4. RESULTS AND ANALYSIS

Figure 4.15. Humphries spike train communities using 20 SSBNs (setup 1). Eachvariation between black and gray indicates a new cluster.

Figure 4.16. Humphries spike train communities using 100 SSBNs (setup 2). Eachvariation between black and gray indicates a new cluster.

44

4.2. NEURAL NETWORK SIMULATIONS

4.2.2 t-distributed stochastic neighbour embedding clusteringClustering of the spike trains from the 4 neuron subtypes using tSNE lead to dis-covering 2 or 3 clusters as seen in figure 4.17, depending on the kernel used beforeclustering shown in figure 4.18. It seems the higher the standard deviation (SD)used in the gaussian kernel, the easier it becomes to separate the clusters. Usinghigh SD:s does not really make sense though since the result of the kernel appli-cation becomes less and less alike the original data, and contains less information.Figure 4.17 shows that the difference in heterogeneity between setup 1 and 2 is notvery big. Nevertheless when SD=5 the FSIs are more independent in (d) comparedto (c).

45

CHAPTER 4. RESULTS AND ANALYSIS

(a) SD=1, 20 SSBNs (b) SD=1, 100 SSBNs

(c) SD=5, 20 SSBNs (d) SD=5, 100 SSBNs

(e) SD=10, 20 SSBNs (f) SD=10, 100 SSBNs

Figure 4.17. TSNE plot with 20 SSBNs used in the left column (a, c, e) and 100SSBNs in the right column (b, d, f). Each row has a different standard deviation(SD) used in the gaussian filter.

46

4.2. NEURAL NETWORK SIMULATIONS

(a) SD=1 (b) SD=1

(c) SD=5 (d) SD=5

(e) SD=10 (f) SD=10

Figure 4.18. Gaussian filtered spike train of an SSBN. The left column contains anSSBN from the simulation with 20 SSBNs. The right column contains an SSBN fromthe simulation with 100 SSBNs. Each row has a different standard deviation (SD).

47

Chapter 5

Conclusions

5.1 Striatal subtypes

In general I note that there were less specific types than I had expected. But therewas not a lack of heterogeneity. The subtypes one would wish for were simply notclear enough to be declared as subtypes. There are certainly markers that mark asubset of cells within a certain subtype as mentioned in the results, but since thesedo not appear as markers for a specific cluster, it is not wise to include to many ofthose. I conclude that there are strong grounds for believing in the subtypes:MSN1, MSN2, MSN3, Th, Pvalb, Pthlh(Pvalb-), NGC-Npy, Sst and Chat. As forthe potential subtypes marked by: Crhbp, Gadd45g, Gal, Cadps2, I leave it to thebiologists to experimentally confirm their existence. Experimental confirmation isalso needed for some of the stronger subtypes such as MSN3.

5.2 Neural network simulations

The answer to the question of whether 10 neuron types can generate 10 types ofspiking behaviour is a matter of interpretation. Depending on what level of detailone looks at and what algorithm one uses, different answers can arise. According tothe tSNE based algorithm and Humphries spike train community algorithm thereis low heterogeneity in the spike train simulation data that was used. The tSNEbased algorithm sorted all of the neurons into their own area except if the number ofneurons was low enough, such as in setup 1 with only 20 SSBNs. The methods agreewhen looking at general heterogeneity but also regarding the difference betweenclustering in setup 1 and 2. Setup 2 shows more heterogeneity than setup 1 in bothmethods. According to this analysis 10 neuron does not enable 10 NAM. Perhaps6-8 NAM.

Another conclusion I draw is that the way we look at network activity needsto be improved. I think we need to better quantify the information that a spikingneural network is trying to communicate, and I think the key lies in the inter-spikeinterval (ISI), and the combinations of ISI:s between neurons.

49

CHAPTER 5. CONCLUSIONS

As a general comment on my own work I would like to say that the questions forthese two tasks that I have tried to answer are not easy questions, and one couldeasily have constructed a master project for each of them individually.

50

Chapter 6

Future work

6.1 Clustering single-cell RNA sequencing data

6.1.1 Agglomerative clustering

I was surprised that none of the clustering approaches I studied had an agglomera-tive (bottom up) clustering approach. This would mean that one would try to paircell together into groups one at a time instead of trying to split all cells (divisiveclustering) into 2 or more groups and then split each group. I think there is arisk of forcing splits but the divisive approach makes sense from a developmentalbiology perspective since cells are created from precursor cells and hence dividedinto two. However this does not mean that we should apply the same technique.In fact it means we need to take the opposite approach. My intuition tells methat if someone created a heterogeneous distribution by divisive splitting, the bestapproach to find this heterogeneity is do divide them backwards, in other wordsagglomerative clustering. The practical downside of doing this is that we have notnarrowed down what genes are important, as in for example BackSPIN, when wedecide the smallest clusters. Therefore, irrelevant gene expression differences mightcreate non-meaningful clusters. If one could figure out a way to solve this problemI think it would be interesting to see how an agglomerative approach would clusterthe cells.

6.1.2 Geneteams improvements

I think the semi-automatic nature of Geneteams is both a strength and weakness.The gained control takes a lot of time and attention from the user but it also allowspeople who know a lot of genes by heart to create a custom clustering in a way that isperhaps impossible for automatic algorithms. Unfortunately the initial suggestionsfor splitting clusters by Geneteams are not always good so the algorithm is not goodfor all users yet. I think there is great potential in this algorithm and its approach.It would be interesting to see how one could make the algorithm automatic byconverting the guidlines I received from Kenenth Harris to programming code.

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CHAPTER 6. FUTURE WORK

As noticed, none of the Geneteams results were shown in this report. TheGeneteams algorithm needs improvement in handling scRNA-seq data with highnoise levels. It seems to work well in smaller datasets on around 100-200 cells asnoticed in the original article [13], but when applying it on 1000 cells, some of thesuggested clusters makes no sense. For example the clusters had no marker or wasexpressed highly in some other cluster. I think this is just a bug which can be fixedsince the idea that Geneteams builds on is solid.

6.1.3 Cell Expression format

The Cef file format is an attempt to standardise cell expression files. I think thisfield greatly needs it and I would encourage usage of this format. However for somereason people do not work with the format in practice. Not even persons from thesame lab that created the format use it. I believe that the people responsible are theones that control motivation in the individual researchers. For example scientificjournals could demand a standardized format in the data released together witha publication. Not adapting to a standardised format creates a lag in attemptsto collaborate and it limits the computer programs and functions that are builtto analyse the data. I do not blame individual researchers for taking the path ofleast resistance, meaning that they work in their own format and ignore export andimport functions or re-adapting the code to the standard format.

If more people start using the cef format one could develop it even further. Forexample standard labels in CellInfo. I would suggest adding always working withone file and having Cellinfo added with a order of clustering instead of constantlymaking new files with a new order. instead add an order and its associated clusterlabels. Attitudes in labs is often free and one does not want to force anyone to dothings. This is a good thing, but in cases of standardization which is important forcollaboration one need to encourage standard formats. This could be encouragedwith the help of the funders or journals. Avoid publishing scRNA-seq papers unlessit is in cef format or other agreed format.

6.1.4 Negative Binomial Stochastic Neighbour Embedding

This is an attempt to inspire mathematicians to develop a negative binomial versionof the t-stochastic neighbour embedding machine learning algorithms. At this stagethe idea is purely intuitive and not mathematically tested. I cannot imagine that meand my supervisor Jens were the only ones that think this might be an interestingthing to try. With these lines I want to encourage anyone working or thinking aboutworking with this idea.

52

6.2. NETWORK ACTIVITY

6.2 Network activity

6.2.1 Auditory encoding ISI:sI suggest someone investigate how much one can gain by analysing ISI:s betweenneurons using auditory encoding. This can be a way of discovering hidden informa-tion in spike trains and perhaps discover a new way of looking at network spikingactivity. One way of doing this can be as previously mentioned to encode each ISIwith a tone scaled to a suitable listening tone and scaled speed. Preliminary anal-yses show that different chord form when listening at two detailed neurons spikingtogether. It would be interesting to analyse what these chords and other featuresof the sound correspond to in relation to measurements of synchrony, entropy andmore.

6.2.2 Community detection algorithm and tSNE comparisonIt would be interesting the compare the performance of Humphries communitydetection algorithm and the tSNE-based algorithm developed in this project, whenthe neural network is operating in different dynamical regimes, expressing differentNAMs.

53

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